{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Beam Spreading\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referring to Section 2.7.3, the refractive index is a function of altitude, the antenna beam will spread in the vertical direction.  This is due to the refractive index causing energy at the bottom of the beam to travel along a slightly different path than energy at the top of the beam. This beam spreading results in a loss in the peak gain, which is insignificant and may be ignored for elevation angles greater than $5^o$.  In addition, there is no spreading in the horizontal direction.  If the signal travels through the total atmosphere, then the total loss is (Equation 2.129)\n",
    "\n",
    "$$\n",
    "A = -10 \\log(B) \\hspace{0.5in} \\text{(dB)}\n",
    "$$\n",
    "\n",
    "and\n",
    "\n",
    "$$\n",
    "B = 1 - \\left[0.5411 + 0.07446 \\, \\theta + (0.06272 + 0.0276 \\,  \\theta) \\, h + 0.008288\\,  h^2\\right] \\Big[ 1.728 + 0.5411  \\, \\theta + (0.1815 + 0.06272 \\,  \\theta + 0.0138\\,  \\theta^2 ) \\, h  + (0.01727 + 0.008288 \\,  \\theta)\\, h^2\\Big]^2\n",
    "$$\n",
    "\n",
    "The expression above is only valid when $\\theta < 10^o$ and $h \\le 5$ km.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the elevation angle (degrees) and the height (km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "elevation_angle = 5.0\n",
    "\n",
    "height = 5.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the elevation and height arrays using `linspace` and `meshgrid` from `scipy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linspace, meshgrid\n",
    "\n",
    "theta, height = meshgrid(linspace(0.0, elevation_angle, 200), linspace(0.0, height, 200))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the beam spreading loss (dB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import log10\n",
    "\n",
    "b = 1. - (0.5411 + 0.07446 * theta + (0.06272 + 0.0276 * theta) * height + 0.008288 * height ** 2) / \\\n",
    "            (1.728 + 0.5411 * theta + (0.1815 + 0.06272 * theta + 0.0138 * theta ** 2) * height +\n",
    "             (0.01727 + 0.008288 * theta) * height ** 2) ** 2\n",
    "\n",
    "\n",
    "\n",
    "beam_spreading_loss = -10. * log10(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the beam spreading loss using the `matplotlib` routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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cGAebrTFXNYQWY0+ZOjbmG6PH8WqMnVZW8wafsuHXlHP1gKIB8FvkRQ0AAABw0VAoII89NQAAAAAWrftIzdVUq5pUsl3jXBWP500PK1ltjedLU0vTxWqOy/WikfxpajXV1S6tTue9kt54XBivdVWqrXkrxluC3MPZ4v4OTR3b57wxq4G10GJ9c2VS5daeFh8ctRraXGlBvecUpqiEttvYKVI87qemSrFrMW9rS07FW2NPTR6RGgAAAACLtsiLvaHFAFrMm87tjXbU9LNJx24RWdoa79Lu8dI+NWnhgKz4vOPCS6z06qt5Wr2v5h6iBPu884Z+qFh62saMsoxdeMC7pik/hM8p9ZXp4cPLbvS+wXzK3ie9b/KvGe+8MWuM+ZoZGtGQxq24Mlcvnos2b1/YU5NHpAYAAADAoi0yUgMAAABcNERq8rq+qDGFM5vaz5PbsJ/2s8nd1jrFbJ8xhqaV1RYUiB1FqWMnSepYnI52fJs3FS3ZmXyc7lzOGHNz/PCMveHGSDPKvZunbM8xZuGBfTIPptqzPfae0zH3xo8u9+D2viG8xfqmbJx0qMbMy7xovV5K5sp/bTFv6+IFWDqeTQAAAGAh+ON9N/bUAAAAAFi0xVzslVLJ5pJbkzdNrUU/m1I1tVw6m7s/zlGyvqj62dE1p2NcPtnjZUSfmmFaVDLLqc2uqHlOp6xclhuvNGapWpnXXG0xSo9tnP05as+aFkqpWb2krkz1JLdOZ5trvHTMJVVCqzVlqttc97/Hx/1wsacmj0gNAAAAgEVbTKQGAAAAuMhM/PGe0/Xj4ql+lqZw5SqZpU06c7el85XS3rwVynJjlM73ppXlKpylt9WM53VptT3vlfibUsPO0quvRdGirXVUjDeX1o9LamhludrUrDEzFFqki3m1yHzKpYgdaoGlot6qXKV66N560U31GpHGLfU4pSVVeGsxL5UEQfoZAAAAgIXrOlITK0VdepNGe3IRGW8/G29BgRZWl/K9clZbPWy8fWqS5+rY+ZKLP3Rp8XQv5pWufj6IzI0x9od+rXvMTNWzZp+5Wo+dW3spgpW2jBq1cMCYYbpewltTFh7o/U0y5ZvOM/YY49eovb+9R4VivfwiPdziBRQKyCNSAwAAAGDRlvT5NQAAAHBhUSggb/GPSymFq7U0rSyXEldKD/Nuys/1mNnnuJqCArGjJHXsJEodW6VpZZFi35qjKMflOM1/yRizN8shyT3sU+5tHtqzRsqnG+6TZdS6D453vJxSJszQsQ9KTY5dC1OmqiwplWhKY/TEGbqOMYsGTFmgYK5iCD2mAOKQLf6iBgAAALgI2FOTx54aAAAAAIvWfaTmaqpU2iPGd27cfybfz6bUi6bU38XLO0a8jpoUtrOpeEc7z/GmqZ2Z9yhaX6H6Wdy35oq3T02LFKmSJWd5eO9jTepSi9S+mvSp3gvTtMiEGbO9SWne3h/b0VNScg9ai3mnrLR2KJXLllydrvs304TmSpukElqKPTV5RGoAAAAALNoiL/ZKkZVp17E7EpSuKRf9qO9nszsC4+1f02I8d8+aM31qMseWXoktPlgZM9pRY5+XbQ/7YHPn7zPGmIUHptxj3GJj/1wfHJbWHtftGLVnTQu99zfpZdP7krWOLE2l9jmdshnYXL+ADitiMhf21OQRqQEAAACwaIuM1AAAAAAXDXtq8rp+XEzh9lQpb/+ZXBpYqZ9Nrt/Meox8sQFvuldufS362bSYK3v+JV96XNqz5vg2ZzrasfPlV4qzts42mCqbccpWGKmax2xo6tg+583VY2bMjf1epZ49vWXZNJNLSemxH0lqqieldTrbXOOlYy61aMA+5x2iudImW8zL89iamT1M0n/W+v9g3xdC+Jbk9veV9AxJd9sc84QQwvPGWEvXFzUAAAAA1nraU2NmK0lPkfRQSbdIusnMnhtCeEl02NdL+tEQwveY2f0kPU/SvcdYD3tqAAAAAOzrAZJeHkJ4RQjhsqRnSXpEckyQdNfN1+8u6XVjLWaRkZo4XezyjOvISaualXrExIb2sylVUyuls3nT1I62Kp7l13d0TfT8nBReYkdRiaVjyx9Xkhv+oNJzMkof1bSo4laTJTK0Z400baGo1i05PPOcN1eNmtYspVS3xVZCk/IpbHP2uFhqJa9eLPnxq1nTlCUml1wJrfX7exl6itRIuoek10Tf3yLpgckxT5L0c2b25ZLuJOkhYy2GSA0AAACA1PVmdnP077EVYzxK0tNDCPeU9KmSftDMRrn+WGSkBgAAAMCo3hRCuKFw+2sl3Sv6/p6bn8X+saSHSVII4TfM7A6Srpf0py0XKnV+UWMKZ1KqUuntuUpm543jGd9fQa2QmlWotFbTpLOUVpZbU+14W2s6ilLWCs03L622Q8JXappvDq3WVdJLhkJsyrW3SGEb2jNujOdgqoyKUgqXV2mtLRp9HowxXzS9pMJ4tU7H6n28GmNUbsuddygVtKZMe2sx78XV0R/vN0m6r5ndR+uLmUdKenRyzB9L+juSnm5mHyrpDpLeOMZiSD8DAAAAsJcQwrGkx0l6vqQ/0LrK2YvN7Mlm9vDNYV8l6YvN7HclPVPSY0IIo+zW7OhiryzXf6b1eC160Zyda/+eM6XN+96o0FBpRCcbSUr61JQiN9t9apyPbekuDv30usd3QOtP5MeOgtX0rRmzZ01pfG9kpcXG/rk+hC7N2/2Hy2MusJceF90/CZ0b+1P9qV6D+7z+egyH9zzv1HNPyyRdM9XfLo6HcdNz5nnJz54Yff0SSX+z9dJ2IVIDAAAAYNF6/JwaAAAAQMJMOuooUtOT7i9qzi8UcJx8PzxdzGtoCltp7bnN++Xx8se1SGdbXfL124nT0Y5v86aiVb4Uc8Mv7I3YRG7Tfw89a/ZZR+97lsfcUz7nftlcql/aQqqLvjVj5ti1eBKmzF9c0humNOZczZxaGJoeJo37P63auS56DxssTfcXNQAAAADWkZprpvv8flHYUwMAAABg0RYTqYlTpC47zylVTMv1iyn1oin1d/GKxyid704JK6SV5eaqTVPbWl+UOnZSSB07umZ7rssnmWOP0pyWKOeldZ+aJUeix7xfPfSskfp8fryZDLkUrhb3sVQJ7iAzLcbOxWvxoPWQ6nbRjZki1WOluh7XlLOEX+7LM+memoUhUgMAAABg0bq+1jOFnYUCVs7IylxKaypFP7z9bGoiMKXIT814W+cfJYURCv1nLq1Oj71S06emxQc9LV71raNHY85b0uKxrRlj7MIDQyMrXq33lE/Zz6a09tLjEhcO6KJoQEmLT7V72LTs/cS79ab8MTb5ty5EUGOqogHSuH1l5pqLogFzmrRPzcIQqQEAAACwaFzrAQAAAEtgyrezuOAWc1FzXr+aXXLFANLxSgUFaooNlFLHcuevz9v9dJT63ngLCrTgTo/bKiLg7VNTkYqWmjLzsOahnivqXXpox8yEKa1jzJ410rTZNDlDU9uk/P2v7QG0aLkHY8qeMCW9pLpdZFMVDRhj/CWZMu2txby4KBZzUQMAAABcaCb+es9gTw0AAACARVvktV4pJaxHufXWVDjbb97d6WIt0tlWl3zri1PRJOn4tsyxR2n+THTcscnF+zAtv/jJmvfd2yITJlaTOrbPOqaqXFY73lwV41qMXbqtJtVtsZXQpPlSV6Z8AbWuNNb7eDXG7qUyZfOuoXNN+T/HHuadem6MbZEXNQAAAMCFQ/pZFulnAAAAABat82u9cHuqlDfNLJfqlaZcSdcOXp23glouPSut6BaPUdOks5RWVkpnqxkvdpSkjp0c519WR9ecHnv5xPnym6v55lxR6VzaV6rHLIyhjThr5/WOP/ZzOrT4krdZ5oUv+lN6MHpsrDh0rgv/hFcY+7mqeX4O9Tk9lMacC7LQZY+NSA0AAACARVvktV68mf1y5RhxdKLUz6Z0m7cfTW5ebzSm1M+mJgLjXV/teKujKOJU6D9zaXV63JUWfWpaf0A017tj7GhC7qGectP70J410rL2KY/dVyb3eLIntmDMvjdTRnSWtMm/NOaUTaRK5trAXlKzpikLFAzFL6q90Hwzi0gNAAAAgEVbZKQGAAAAuHCofpbV9cOyft7WuRS5NLN0s30pXWxM/hS2wiZ6b48YZ0EBzzznzZVfgzcVLS0i4O1TEykUHthS2mB/0aLZrdP0So9tTfrYmD1rpPyavFkOrccrqe0rM1RtUYJcqlvaTqqLvjVjpxKNWRkiN0/tXIeqdbpcjaFFA/Y5b2gq2pRzzVU0YOq50YuuL2oAAAAAbBCpyWJPDQAAAIBFW8y13nZPmP3Tys6mXO0eL+3NUup1E982tBJaaYz0uFzfG2+VtHSeXDqbN01tdcm3Pmk7He34Nm/FsyiP5TjNcXHqvTeNl/dl1rqfz9C0qn3G6CGbpKTF+mqqv+XGTscfOvbijJ120iKFbaoX61yVxuYsTTimKVO9DtGUaW+luQ/wOaD62U5EagAAAAAs2mIiNQAAAMCFxp6arK4fFlM4N9WslBLWi1x6W7n5pve4/dPKSulsNeOljqIUs5NC5bKja6ImqifOl2LpsNYpVz1qHUUvvV1aV/LyrqF1ytSU2S5zpezVjO+ttFab6hZninZXCU3yl3vzmvIFdOCpNdWW/PhN2SxzzFKUrfXYDBW96v1POAAAAAASkZqCxTwscZTAG40pnZO7rVRQoLbYgLcAQPx9abN9rp+Nt6CAV21EZ2uMo+SxyPSpubTaPu7K1sTeggKF2y7EhumMQ+xZI/n71pTWVNMHp/XGfq/SWnv84Hk2Yz4YLUJpUz5ZrSMXvY9Xo3XRhNL4F/3N2UMPm4v+HBw2CgUAAAAAWLTFRGoAAACAC6+/7eNd6P6i5rxCAaWUsB7lUsdKSv1sWhQUqFmTd31njt0qIuBNK4vGLxQeKJqyv8tUltSzpnaMmuwUabq2Gy2ykcbsK1MqwlBae02qW2mu7ooGSHWVEbzGzjfMzUVqzak5q24MNWV/lykLFAzVOv1T4j1zWLq/qAEAAAAgCgUUsKcGAAAAwKIt5lovTpG6XHFOmpZWM15JnIJVSonzpoiVUsJaV0nzprN509RWl3zri1PRjm/zpqKluStRXstcfWpaRK/nmjfWomdN6zFaVC5rbcr15caYK/tjccZOzRr6JE+ZCtND5bLSmK1zQ2tNVWltn7F7TzGcKu2txbwHgEhNFpEaAAAAAIvW9bWeKUSRg2tv//l2T5jhO2tL43l73ZQiMPl5vf1s6vreeCMwuchPiwIFR0fJGJmN/kfXpD1xTl1p0admzA+3Sn1bvOZq0Bybq5H62Jvye4z8zPVh8JhFCQ5Wiydr6Bi9RDGWbMw3eI8Rg6HRE2m618+cr9sW/wOfGJGaLCI1AAAAABaNaz0AAABgKehTs9NiLmq8aWa5dLE0hStOZ2vNm8JW3EQ/sBjAPmN41aSpnRnjKEpbq+pTU3k/chHmXjIFWiu9s4fe5xab1MfO1hhzr3PNeN7HzNtXZozHrGbtNalulhw3Wd+aUorLXI2YUlNtCJ+zYdNcb84aU429z/hT9pUZOtecedWH+j93lCzmogYAAAC40NhTk8WeGgAAAACL1vW1nincnl4V95IpVSTz2q54lh/P2+vG24smd/4+Y6wq5sqdn47h7UXjTXsrp6LlUwqzfWvOnBMdd5zmtTjUvgN6qFZW0qKyVe6p671nTWn8UnrXmOOVjNmugWJYjbV4QKfsz9F7f5PWWqe21Zgy1at3PBajIFKTRaQGAAAAwKJxrQcAAAAsgYnqZxmLuajxVj8rpYvNpX2a2u6nrdSksyZdzNv0s2R1KW2q6bzPUTPOyyfOl+mYFb/2mau1MVPdWjcsbVGYx7uGHvvdxVqsb8xmmbWV1mpS3Ur3I84anawSWknr8n61Y0yZgjNVGcCxxxtTj7mctWsa2phzruaY+8zbw2sGU1vMRQ0AAABwobGnJqv7h2VXhKYmGlPalF8aL1dQYP397qhGWmzAG+HIjZGe7+17UxOBKfW2aRHROYo2+p8c+15+l1an41+p7VOTm6qPYN64xo5g1XwgVnNO7ab8HttiDP0QsRQI4ANKpxZP5NAxWnz6T9GA3cbosVNjaFRE6u/5mjJC1AK/FC8KCgUAAAAAWLTuIzUAAAAANvjrfafFPCzbKU7XFo7rO5+olKaW20Sf3qfccd6CAi3UpKmdGeMoHwY+yaWZFXrbyJnOtr0I53E9Rqy9d3fMnjVS+54zsbH3iNb0wWkxXs7YWSelxzO39tpUt9xcpTTC7ooGSG1SV4aOMWU60pib/EtjLi1dbGhVlDH+p9LjmlrqPS0Pc1vMRQ0AAABwoVHSOWvyPTVmdl8ze5eZ/dDUcwMAAAA4PHNEap4i6SbPgaawM53Mm2JWSvUq3VbDW0HN26fFncJVcZw3XcybzlY67sxzFV1Glx6LVZRmdnyb8/k5ivNVbPu2oVH1HmOaU/bembKw09BqaqXzWme4TJkxU0ptW1IGSZd6qITWYozWL9xDMmb1t7Ery9WM36Jp2Jg9bHqohDb13I1Q0jlr0kiNmT1S0lsk/eKU8wIAAAA4XJNd65nZXSU9WdKDJX3RvufH0YDL0c9LvWNqlMYr9brx9qLZHnt7vO1oT/6p8R4X3xdvBKYU+cmN55035e1Zc3RN9NyfVL5kc6ddhA8oU7nHooeeNfucN3RT/thaFCEY84P70of1vT+2s2nxKW+LT6in6jkzRuRnzEIEPfSYqRl7jPGXZMoI0XlzLwCRmqwpIzX/VtLTQgi3lA4ys8ea2c1mdvOfv/HKREsDAAAAsFSTXOuZ2f0lPUTSR513bAjhRkk3StJH3HBNN0U9AQAAgNkRqdlpqoflQZLuLemPzUyS7ixpZWb3CyF89L6DeQsFlDbv59LZxhCv15s6ljt/fd7+/Wxq0spqx6tae9KzJten5tJq+7itWF6ut01JmjWRs4TMgKn61vTQs0Zqvx+1dc+akqFjlHq9jC23dm+qm7c/TlLrY7q+NS020bceo0VqTeueKz3q4f7O2WzKc84+59X8oprrtXQRi18gNdVFzY2SnhV9/9VaX+R86UTzAwAAAMtGn5qsSfbUhBDeEUJ4w9V/kt4m6V0hhDdOMT8AAACAtszsYWb2UjN7uZk9Ycft325mv7P59zIze8tYa5klKy+E8CTPcXGfmlzKWakimVcuPSwdL11DrtdNWkHNWxlttTXX8EpjNaluJbnxSmlqKXfa2lZlNG+fmngdyTnHaS7LnpaQvzpV35opW3DUpmnNVWBpqjYj3jHS8+ln09jQB7TFEz5lus9S31gtjF2tbGiql9RfytWUaW8XSEfVz8xspXX/yYdKukXSTWb23BDCS64eE0L459HxXy7H/vpak/apAQAAAHAQHiDp5SGEV4QQLmu91eQRheMfJemZYy2mk2s9AAAAAEUdRWok3UPSa6Lvb5H0wF0Hmtn7SbqPpF8aazH9PCx72E7vurZw3O50rvPOG1NpTd6qZvH3uYpk+4xXU9WsxN0c9NLu+3HmuCit7Pi2yt1xYzacXLLSb4AW2TQ149VkHnirgXkzSOYar2TsDJzc+KW116S61Tb9jDNIJy323+KBHzpGizSjJaV6tTBG49AaPT6eU+YCD1U7b4+P+yJdb2Y3R9/fuGm9UuORkp4dQhitXuciL2oAAAAAjOpNIYQbCre/VtK9ou/vufnZLo+U9E9bLWyX7i9qrkYzVs6og3/c3RGTdJ7Sbbk+OKViA97ohzfa4Y6KVBQUKEV+agoZlI49OkrmOt59X46uSaNMp67U9KkpvQOm7P0xplIvnhb3sabnTO78fcZovRe5tSnXl3sOxt7bjEgPDYym3DheE/mR8mtaWmRlzF8stW/coT1saqI20rJ62BxI1Kafks43Sbqvmd1H64uZR0p6dHqQmX2IpPeQ9BtjLoZCAQAAAAD2EkI4lvQ4Sc+X9AeSfjSE8GIze7KZPTw69JGSnhVCGDV5uPtIDQAAAAD1VihAIYTnSXpe8rMnJt8/aYq1dPSw+MXpUpeT20rpYr2pTVNz93qpKChQSisbWqBgr7UfReuo6lOTyKSzFbV4+bTIShj6Lh17DUP3PY+d8eDVQ7qYd67W+8ZL46dj16S6lR6zmgIFXRQNkOZLZZmqIVLr1LEx9JAu5lX7vPWw9tZ6/J8Alm6RFzUAAADAhdNZpKYn7KkBAAAAsGhdX+uZwu0pWmma2VXe6melVK9cFbN91FRQ86Zp+fvZ5I/zVknzKo2XS1NL+VPR8s+Ju2/NUZyjEuWujB29bvEOmyrCfig9a6Th6V1jj+c5v3aM0loPMYulS7W5fa31WPXpENPFatbaSxW33Dn7nNfDc+XVSwrpAERqsojUAAAAAFi0xVzrbUcu8h+Vtoi6eMfzRiS8vL1fvGsYs6CA19kI2e6X3OpS2n/G2c8n6ltz+aTi5dx6A/wS5PrW9NCzpnaMuVphTNlaY+wPQ2uKErR4vr1yc1ly3GyFA1r/wqjpA1O7htZzHcp4NcbezD5lVGjoZv6x19fieSw1cutY33WwZkOkBgAAAMCiLSZSAwAAAFxo7KnJ6vphMYVzCwGkm+OlazPHpalj0/WzyaWwpWvKpVx5+9m4U70qCwrk0tm8hQzSY0vHHUXFAU6cPWYurU7HvpLe6O11E6uJSk+5R7L23Tt0jS1S9lqP0WKTf2tTrq+m18vYcve/lz30kxqz4ZDXwT64E+lxYz9O0cMGnV/UAAAAANggUpPFnhoAAAAAi7aYaz1vuli+v8vutLTSPOlcpdtKVdK8VcNyY3grl9VUOCuNUUora111rdynJh9GPvGmlW31uonOOU5LJw005Ttqrh47tfNO2cNlzKI9Y6aLpeYq0rSkthOp+C19kJXQpD7K7NW8iEtj9l7CsFaPldaG9rCpzTMec30XDNXPdiJSAwAAAGDRuKgBAAAAsGjdp5+tTjb5HZlQW1rx63JunOS4+LzcOWMopdH5G276KojljvNWSfMqjeet8Fbb6HMVpZUd31YRjy3d9aWl3dQoVXgbWimrtvJWTXPQ2iyH3nr6eR+zOSuI1VRaKz0uvTf9nNRUaTdTVl1bmimbnnrG3mf8JeeN5tQ+zof4WGxQKCCLSA0AAACARVvMtZ43shJ/4j/0HGl7w37ptnyBgu2ITC5ScXZN+YiON9oRHzdmQYGzY+QjSaU+OFtjXIruS3RYqWfN0TXx2rddqelT43139Pgh0JRrz8019l7Smg/iWkRCxhyvpMUHj6UIxwF/sDm90iftLR7oqaIJYxQomCs8OlVkZYxQbg9rz52zz3lz/ZI5sF9uRGqyiNQAAAAAWDSu9QAAAIAlMFHSOaPrixoLQavjTahwddpnZpVJ+ypJj/OeN6bSmkppat5+Nu5Ur4qCAjXpbOmx3oIHW+MlPWvq+tRECulsbj2+i8ZuhzB0rjFTx1Jz9YSZMitmrlYYpbXnUt1q++PUFCiYrWdNC1P26uihcVKPeuwxUzN+TX8YaboeNmOvDxdFj3+OAQAAAEixpyaLPTUAAAAAFm0x13r+NLM4jHlt9rjcOWmlsZrbvBXUvP1hvD1cao/zVknLpYt5e9Gkx+bmTRXT26K0MncqWuwozUmJ8lUuQnZFbMyeNVJdb5EeWxRMmS42tB+Lt9dL7fhwyr1opiyLt+R0tqVWQht7/IuefjU07U1a7P/oF/PX+7SI1AAAAABYtK6v9SxIRydXJG33mSlFT7R1XL53jDeKM6Vcv5yaTf77HJebqzai4117Tc+aYhQoVwxA0uWTipe695TeP+HuoWeNNO7+0doP37yRkB4iK7n1SP7N9l6lMXK3eaNC3v44rQsUWHLcogoHtHhh1Mx1EYoGTPnYlvRYlGBoD5slRE+6/jN4N/bUZBGpAQAAALBoXOsBAAAAS0CfmqzFXNSsTqI8gsKTGac0Xc4fVkj12s6N8I6XG3v9/e50uTQlzt2rxdnPxrsp3ztvDe999B53lKSYnTj7zFxanY5/Jb6hprhAqvdfLkvqWbPPGEMLD0jDM02mzFwZmtom1aVtobHSkzBVSteU/T6W/KZroceiBEN7xNSkou1zHj1sUGcxFzUAAADAhcaemiz21AAAAABYtL6v9YJ0e9bQdac/LqWLxXIpZpI/lSw3XmkdLdK5SpXbavrUtKim5q2SVq7C5nuc3Mcd7Q5Nu3vWnKmYFp13nJZLumByfWtapD7VjDd2JsNcldDGrlY2VG2ltanaadRWXYvf3ouqhCZNV6FsjFSvufqszNUTp8aU1coOFY/FRdT3RQ0AAACAU/z1vhPpZwAAAAAWbTHXeqvjKPy6yjfOTFOhPMdN2aSzlBLnTWHzNumsSfXyVkkrpakNrc5WOs5dIS5JKzu+rSIl0PvuWFpke6pmnC2KD9VmeLTONGltqvV5G1jWjl8jfSvGb9Wapp+LVnoSprzDPabq9P4mjo291qlS3cauNDa0MefBvPGHoaRzFpEaAAAAAIvWdaTGMoUCvJGVXH+YfZTmyvWcSefybtLPzXs2orN7jBY9YbyRH29Ex7t29328lERgnM/r0TXx2k9dadGnJrcBvldT9a2p6TfTaozWfVZ6iKzEaqMdXqUN9kv94LT0mFE0YKa5DmW8GmOHSodGRaTpeiWNvb4eonkNUdI5i0gNAAAAgEXjWg8AAABYAiI1WYt5WI5Ortz+danHjL9QwPDUtKHStZY233vGqN2UX7MR37ums0UEfPfRe9zRmT4zm3Oq+9REjhfz9hhfKcWuJtWrRTbAmNkL3nS21uOVtHjM5mqtUVp7LtWt9/44o8gtvve0oItgjJ49NXrM/1zSa2auCimYAn+1AQAAAEtB9bOd2FMDAAAAYNG6j9TYjjSN1UlUGWy1fUCcmrZdycuX71HqHeO9LZ0rV0GtJsVsnzFyaWAtqql5K7qla89VTau5HyVpnxp3OlrsKC6DZNnDuskAqDFVz5p0Lu94pfXVjNFj4aSxq5V55dY7dmW5ueQes/StvqhqaFOmSNW8wEtztV57L+liXj1WWhvaw6amf80+5/WYijcB9tRkEakBAAAAsGiLudZbxRfh12UPcxcKqInijC23Jm8UoxSBqYmKeAsKePvjlJQiOqXj4svyYqGETEGAyycN3gIt3kVDPyidct6SFpGVmqhDbSGDuT7o897Hoeubc89y7rbSh7I1/XFqCxR0r7T4qfqseD9Bv4ibr6cMxXrG3mf8Rb8xMlo8FgtBpCaLSA0AAACAReNaDwAAAFgCIjVZfT8sQacRxCjlbHUc9ZhZ+VKp0hQp6dpzz0nPS/vZ5G5L54pvK6W9edO2/Jv0h/WE8aaz7dP3xrt273Fb50QpZifOHjOXVtvP1ZX4m5riArVq3ok97GdNDe2/Io27R7Q2Q6G3dLEW2T6t+8ocqrhwQJdFA1qkN02Zh3kRKncMHbvHjfNDiwZIdYUDDiU9DlMg/QwAAADAovUdqQEAAABwiuabOy3mosZf/Sw+cHeK2fq4XGpa/pwpldLecqlktRXEanrHxLxpaumYNelxqexjcVQ4x5tWtlUxLT0nykk5lPL4Y/esyY3fS+pYbK6WGSXe7I+aCmItlB6L0rw1/XGmrLrWhRYvoBYpPd4xDiV9aKoqc+eZqodNTZqbNF0Pm33u+wXtYXPBLeaiBgAAALjQKBSQxZ4aAAAAAIvW97VepvrZ0clpjaqTkyRXYBV/uX8eQXpOqVpZfNtl55ilimxxmlmpkeT2Od4UruEVybxzleSqppWeq2JTzYp0ubgR5/FtDRJTe6xcVnO3eukLF6tNCxqaUVGb3jXmeCUtslNq0rYuGku+764aWos3HZXQpjFnZ9ycQ32zH1gqGpGaLCI1AAAAABZtMdd65vwEMxc9ObuZve1u0DhiUNo4P3Ts9PtSFCMXgSlt3i/N6y0o4I3otO5Zs7rke1y2xr4mvY+nrozZp2bsd96UH7Dl+tZMGXWo/dCz9w+Kh37AWNps30JNUYLW/XGmLFDQjaGRhikLD7SuutFaj1VBSqb8Bek1NCpUUzRgn/MOENXPdiJSAwAAAGDRFhOpAQAAAC409tRk9f2wxIUCInHPmtUqOWB12mfGWyigVAygtLG/pqdNOU1t923+1Ky2PWG8BQW8aWqludPHduhxR0f5576uT83ZldzuON09fCDG7FtTGrt16ljMm1o0VybMlOurTdvCqfit313RAGn4EznXG6Gk93S21Ji5pr2nuo19P3pMxcOc+r6oAQAAALBGpCaLPTUAAAAAFq3/a72r6RdxhPC6XQeu5VLOzlYQiwfcP40sHTMeb6W6amD5efL9bLy9aby9XrypYzVpaumYtX11ao7bOqeQVlbVt6bmXTRlRaXaKilDo/Kleaes5DVm9sdc6WK1ldXG7IlTeixKa58q1a111bVutOi5MmU1td5LCU5VmrDkUKqVLUH/fwafQaQmi0gNAAAAgL2Z2cPM7KVm9nIze0LmmM82s5eY2YvN7EfGWgvXegAAAMBChE761JjZStJTJD1U0i2SbjKz54YQXhIdc19JXyvpb4YQ3mxm7zXWehZ5URMXPDs6urJ1W9xw05tiVqpwlqaSjSmXwuZtJOmvIJZPK/NWXfMqrWloelzpuDQGWdOMc9RGnFP+Qho7UyD3Mqmdt8V4Y2aGzJXtUtLi/tY0yxxTKaOppunn2A6+Eto+YxxqqlJOL49Lb4977S8PGnMuyAMkvTyE8ApJMrNnSXqEpJdEx3yxpKeEEN4sSSGEPx1rMaSfAQAAANjXPSS9Jvr+ls3PYh8k6YPM7H+b2QvM7GFjLabvSE3cp8ZbKOAk+thuFX+5/XFeHJG4LB9/X5n8XCdbUaF8f5eSVWaM0vmtN+/X9KIpzXX2sd390vQed+a8qDjAyXHFy77Us6ZmvKVp0VfGO/aU7TS8m8C9H8TONV7OnG0set9gn3sOvBEiqcP71eIJn/LT/t43+fcS+ZiqKMHYfWWmLFDQy3PXXjDpZLo/O643s5uj728MIdy45xhHku4r6UGS7inpV83sr4cQ3tJmidsTAQAAAEDsTSGEGwq3v1bSvaLv77n5WewWSS8MIdwm6ZVm9jKtL3JuarpScVEDAAAALMO0kZrz3CTpvmZ2H60vZh4p6dHJMf9D0qMk/Tczu17rdLRXjLGYfh6W80SRSoszzNIIZpSa5k0xW2XSyM47b0ylNflTs3anevkLCuSP86azeVPqSnOX09n27wG0OsqHvU9qCgIcxbuCLXvYljmj4TVPSYuMh9y8tRkPMW+qVyr3PHjT2bxZGHONV1LbV2ao0tpL87bujzOm9NdAd4UD5mqcJC03FWiuN3tp/Napc3NVBRnb4aaizS2EcGxmj5P0fK1/G39/COHFZvZkSTeHEJ67ue2TzOwlWv+G/hchhD8bYz3LuagBAAAALrBg0vFqqjpfV849IoTwPEnPS372xOjrIOnxm3+jovoZAAAAgEXrO1ITVz+LK54VKqGtjqMbV6e9adIKXen3OblKY+vvd/eVKfW6yZ0j1fWIGVq5bJ/jcnN509TWt53er5q1e49L+VPTdo9xuXUCaymVamxjRt9rUr284+2TCVFzXu/VulpUVhuzJ86hZBmlllzhbfATPuWTuuRczhbGTJGaswxizpRV1w5LMNPJ0VR/vs+1CaMOkRoAAAAAi9Z3pCaW+RSsVCjAH43JR0/mUlpT6030Q3vHeMcrrcnbs6ccjcmvb3VpWPGCS8kLbSvLtKa4wJK17lnTYrza/iFjfsDYumdNSYsxxow6lMbOrb30AXpN9KS2QEELceGA7ooGSMMfgH2iHd43XeueM15z9cSpMXaUaaq+MrX3o2auw4j2nKwu2N8dTkRqAAAAACzaciI1AAAAwAUWZO59whdN/xc1u9IgCoUCjk5OE4Pi7U2lzfsl/o39pb4yu287SY6r6e9SKmSQG6P15v36XjT5udI1+tbkS7c7yhQDkGr71MTjpedn+tb0Uv6/h541talOU2Y5zJWRMnTvcOt2JOkYYxcl6E0pc+XgiwbsM8ZcDYJKenhB9tLbZ6qN/UswZ9UetNb/RQ0AAAAABVn1h8mHjj01AAAAABZtOZGaTMqZFcL8q5PoxsJFbSklrAdpWpm3n03ufnkrkp1dx+7jvOlskr+qWW4drY87c14mNe34tsafivTyzltqzxqpfepYzFtNzbumucYrmSuVqrT20nNV0x+nddW1Fg6+Eto+5qqE1lue6HnGTPvrsT8OPWzO461Ge9EQqQEAAACwaFzUAAAAAFi0vuNXQaeRx7jKWSEaucqkqZUqkpV4q5VtV1e7Nr++7Dnb1dW8KWa5sdfn+VKzvClh8XE1DTYl//3yVlerOS6+lPdutju6Jn0sTl2h+eZuLRpJ1o5Xkz51KBkuNSlc+5grbatHueekRXrgqFqk6tS8IKXDf2F4H9seG2fWjL3P+FNWXeuh2t04KOmcR6QGAAAAwKL1HamJ5XrTpBfu0W2r4+jGVV30pBR18YyXjtni6jq33hY9XLy9Y7zFAErRI3dfmarCCL6Xdtqz5uS44i1R6Huz/ZFttEO4xw8rvS/NFmsvtQZovem9xPsh4Fw9SKZc31T7hdOxc2tv3R+ndYGCFtI2Vt0VDvB+It868lMao3UkpJfIitehbOyf0mFFbYjU5BGpAQAAALBoy4nUAAAAABcckZrd+r6oyRUKKPWmyaSplVLCWmzsb2F7rvy83gIA3k303rSy1qlz3jS17fO9j0U+BF5Mvzvafd5J62IAPb7zWkflS/exJkOhxXhjZ9NMlS42ZcZMbdrWRVOTYteNKdNzeun9MtRcFUNqTLmx32to0QCJXzpI9finFQAAAIBEkLkrt1407KkBAAAAsGjLidTkQvaF6mdHJ1du//pyYeg49SntZ1M6L1bqZ5O77WzvnP2fjrrKYMMrkrXoRVM6rlSFrea4wecUKpxd6J41Upu+Mp6xa1sUxEqpPzVz9VhgyZvqVJqrRdZJ7vEsPRaltdf0xxmz6lp6WwtxNbTuKqFJfaaOzdUQaky95MnWqBl7yvvRolLfvNbVz5bz5/uUJovUmNkPmdnrzewvzexlZvZFU80NAAAA4HBNean3zZL+cQjhVjP7EEm/Yma/HUJ4UfaMuFBALNezRpJlPplcnSTRk9Xp92NHcWqU5j1pHMXwFhSo2eRfu7H/JDN+zfpKVpeSF0z0jhi1Z81x2qCiQ1P1rWnRs6b2E/SaD/pqIiEtiga0+HC1xRg1EY6LpjYa1YUpN7a3jp7MFbXpJQrExv79lf4H1C+qn+02WaQmhPDiEMKtV7/d/PuAqeYHAAAAcJgmTcozs++W9BhJd5T025KeN+X8AAAAwFKt99QQqdll0ouaEMKXmdmXS/o4SQ+SdGt6jJk9VtJjJel97x7dkEs5K0Qzcz1rzhxXmQPg7SuzyqRjpX1uvJv082sYnuqVGzs9zrvJP00Dq9mkH/MWV0iP2z7HWxyg0OtmaEGAJezxm6pvzZSpVKWeKyUXIatlrr3DubXX9seZqkDBebcN1X3RAK8pq2QsyT4b1g9lY7/Xoaa6YWyTl3QOIZyEEH5d0j0lfemO228MIdwQQrjhPe829eoAAACAPq23m68m+bc0c/apORJ7agAAAAAMNEkSjJm9l6QHS/ppSe+U9BBJj9r8K9uVHlJKK8vctjpOUsJWvpSrUrrYVEprqklTK1191/SOKaWzldLFvGlqubF3zZ07Ljd2bV5qrm/N5ZMl5JWNqHT3h/ZcqR1vzEyG2nS2oaZc36IreXWiJsWuCz2mjnnX1HrtvTwWY+ak9rL2WG1u7Zgpe+jVVH+BBa1TzZ6qdXTo1ZK+MoTw3InmBwAAABaO5ps5kzwqIYQ3SvrEKeYCAAAAcLEs51Ivl3JWCNf7q5/FB17rXlIuNa3ULHM7vStpCJq5zZsi5W3S6T3OWyVtH7nUOW9VszQtLZdKV3tcvMusZpPcpdX2Y3Yld2BNY885TdWIU5qvklcPWR2t08W8BZWk7TWN2VexNG9p7a1T3YZWXZPaP2Y5aa/e2aqhtXhzTtncswc9NObsvVrZ2FXXDuW1tEZJ57w5CwUAAAAAwGB9f1y8rlu35ozO5CI6Ryfbn5mfnESDrOIvfdGTKXnX1GJzvLegwJi9aNZj+IohlAob1By3dU6mGIDUok9N+lFr+lFsZ6bqWSPVRVZqxxhzo/tcPWtKWtzf3ja9zxU9qTVlNGo2Uz7w3jdQj82hhjqkjf3YB5Ga3YjUAAAAAFi0viM1AAAAACSxp6ak74uaOP0sF/lMfx6lnJkzfB+nJl12Li09L04D8/a6STfi526r6UWzzxj+47yFB3b3opG2U9VqUuLa3I98GN39WGdS045vu+C/aLy/UVr0rGndcyVWm9rmTR/yZnW0Hq9kzIyh0ti528be2+xVeg5qiiG0EGerUjSgYry55q0Z77wxa0y1sd+r9g3TYy8ezKnvixoAAAAAktaRmprqrBcBe2oAAAAALNoyIzWl/jOZSGLSPkSr+Aer0940Z1O44rSydHB/T5uWcmuqTVPLXfF708rSeUtrylUhq+0rk7vP3uNK59XkrB5dk96PU1v194ZWT5tTaektIvm530r7ZGDkxmiR6hWrTWfLrWPK8bzpfLV9ZcbUOiVsrvvhfX4OphKaNPzFfxGNWSKxx/44c/WwWQ5vFdeLhkgNAAAAgEXr+1Iv6PQTqtwnVYVCAcWITuRMV3mnXMRkVYxOnOz8eem2tD+ON4Lg7WfjPS4XgfEWAzh73v4RnVRNX53t9RTGvhSdkxx2cjzwrXOm0EC89gvQs6ZFj5nSeFPtM20d+SlZWtGA3HqX1lcmVopu1RRDaKG7ogHSuFGW1pGf3sOttQ5lYz9SVD/LI1IDAAAAYNH6jtQAAAAAkESkpmQ5FzXHma/TtLJMWkZaKODo6HTbdtybptQ7pkc1aWq1m+1resx41aapxbwpa9vzbj/f/j41u8PtJ0suANBa6xSzNKsj5k3vqs2amKuHS+sxWmR/zNVXJrf21ilhc+5Xr7mPizNX75e5zNUTp8aUG/u9ascm1e0iOvcvPzN7L0mfLOkjJd1N0lsk/a6knw8hvGHMxQEAAADAebJ7aszsQ83s2ZL+QNLna33Z+4bNfz9f0ovN7Nlmdr9JVgoAAABccMdaTfJvaUqRmqdL+o+SPjeEcGt6o5ldJ+nhkp4m6eNGWZ10Gm3MVS8rheG91c9OokEKz2FttbJc5a2zPXF2V1ArpcT5+6/4Urhq+t6kSmlgNdXKvNXUts/Ph6nL9z9XJc2blpZ/QW71rFlymlopDSzWumfNlJW8atN9ess02SfdrqZamVfrvjK9mKvCW27etHBiF9XQekkd866px7XnjF1pbapUt9qeMj3cD/Qke1ETQnhg6cTNhc6Pbf4BAAAAGNG6UMBytsRPiZLOAAAAABbNdalnZh8p6dsl3V/Sna/+WFIIIVw7ztISuepnpeOilDNLUh5WmeNKKWYttEjv8oy9/v50/BapXrm0Mm/lsvQ2b7UybzqbPxXPlwK3dc6l5HUQLaOqEWchTe1s7lNnzTjH7uNWM1fpaZyqEWdpLm86W+vxSlpU+ZorA6f0XLVOCZsrc4WMmchceZ1zzXsojTlrfvlK41Zdm7O8YRuUdM7z/jX2TEk/LumfSXrneMsBAAAAgP14L2reW9ITQwjTbjsM2l0oIP70Lb04vy5zW+G41XF04yofeKrd2C/tH8yK5ypFj7xRB28/G2/0pBRxKq0pF1nx9ospHZfb5L8+bvd9OfvY7h91yfWskehbsyX30NZ+UDhmz5UW5/TwIW/rjej7aN1XpndzFUMozRsHebsoGiAN38y+/E/a99dbD5uxN/bjPERqdvPuqXmGpEePuRAAAAAAqOH9WPpbJP2Gmf0rSX8S3xBCeHDzVQEAAADYEmSL7CEzBe9FzbMlvVLSczTXnppcCkQazfT2pskcV0oxq0kjS8cspYF5U79ySmlq3t4s3uO8vWNK6V01aWr1a9+/T0+sNtSb61uTzkLfmowWRQRaFyIopWN5s0R6H69krr4yuXlL2UitU8Lmynw6qIyrqSogtE5nW1oa3Zj5qr2sPdaihw2WzvuX8/0l/dUQwuUR1wIAAAAggz41ed49Nb8m6X5jLgQAAAAAangv9V4p6efM7Dk6u6fmic1Xdfvg2p1K4Uwx81ZJOzq5cvvXpVBUuW9L2342pXlzVdf8fVqGVxobmiqXqk1Ti7VOWcudI2nr44Amua3uvjXOnjWlalOtDU0zapEelqqp3lVax9BMk7nss76aamVerfvK9K62wtvQ9MDSvF1UQttHj2UBayzpfvTYH6c2XXHJpRTPR/Wz3bx/jb6bpJ/RelPJvcZbDgAAAADsx3VRE0L4h7t+bmZ1O+e94j41uehMegGei84kER3LfNq4OkmiMavT770birx9ZdJISK7XTfUm9UxExxvtGGPz/tD75e0rk0ZWcve5prfNmTUVoiyz9anpsRt56z41sSkbUtdEgXrcY9ziQ9kxow6187buj1MzVwtjzpsGfLvoYdPLLyrvmnpY+9KKEsQOO5IypvWeGiI1u7j21JjZd+z42R0kPbf1ggAAAAD0z8weZmYvNbOXm9kTdtz+GDN7o5n9zubfF421Fm+hgI8ws2+4+o2ZXU1H+5P8KQAAAAAOkZmtJD1F0qdoXVDsUWa2q7DYfw8h3H/z7/vGWo93T80jJP2Cmf2FpO+V9LOSXirpsWMtrKhUKMCbphbJ9aw5c9yZDfu5VLJxs/JyBQq8KVfefjb+VK98QYHSmlqkqXmPqyls0CQFsFgA4NTlkwWXZ/QufWgtjTF61oy5V7XHnjWt9yzXqO0rs2RzFUM4mMdzrs32rVPRauY9b8waU/WwmfKXR8lhpbp1ln72AEkvDyG8QpLM7FlaXzO8ZI7FuCI1IYS3an0V9g8l/Y6k3w0hfHEIYRE1VAAAAAA0dQ9Jr4m+v2Xzs9RnmtnvmdmzzWy0gmPZzz7N7Mk7fvybkv6upDdfvX3Uks4AAAAAbteklYTP9WZ2c/T9jSGEG/cc46ckPTOEcKuZfYmkZ0h6cLMVRkoJHbkrqZ+Nbhs/UnNe9bNSSL0iTW11nFQkW+3/wvH2lSmlgZV64HjDjrkxWvRwqa2SlksDq01Tq1t7Pvyc79lTqpI2/JfLpSgH8krhOHfPmtp0rBpjt0oYOk+LnjU1j2ePWTIlQ9Pb5mpxUZq3dX+c2rmGGnveLnrYtC4ReEh662HTS3+coXnB+8x1Yb0phHBD4fbXavt64Z6bn90uhPBn0bffJ+k/tFvetuyfNLkyzgAAAACmt95T080+3Jsk3dfM7qP1xcwjJT06PsDM3ieE8PrNtw+X9AdjLaaUfnb3EMK51c28xwEAAAA4DCGEYzN7nKTnax3//v4Qwos3W1RuDiE8V9I/M7OHax0W+3NJjxlrPaVLvV8ys/8l6QclvTCEcHtWjJld0rriwRdI+gRJHz7WAm93kvk6VZGm5q9+lm+WGadVeZt0tpBLbZP8aVFDK42VqqSlaVvbldHy89akqW2vtS5lr5RWlz3nUjRX4R01aSPOqSqS1WpRycw73lSNOKX84+nNeJhrvJLWjT5bNMtcshbV6VrP26WheXVzdbztJY2u91KHJUOrrtXOtQydVT9TCOF5kp6X/OyJ0ddfK+lrp1hLqfrZR2ldku2/Snqrmf2+mf0fM/t9SX8p6amSfl/SR4+/TAAAAAAXgZm9h5l9mJm9/yaYcq7SnprLkr5L0ndtyq/9dUl3k/RmSb8XQnht7txmgs6/SE9vH1hE4Ohke5v2yUk0iPPCOI0SePvK5KIupQiRO5pQiOjkNuW32LzfgjeiEzvb9yZ3XP5TmtzYu8b3KPWsOb6tn09dBhkzQtQiulN6mFvvTe29aECLD4rnijqUxs7dVtsfp/VcLYw5bxdFA/bR+xutx6ognrFL4/fY8Oti6SlS05KZvbukfyrpUVo3fXyjpDtIuruZvUDSd4cQfjl3vutPkBDCa7RdhxoAAAAAWnm2pB+Q9LdCCG+JbzCzj5H0+Wb2/iGEp+06eXnJhAAAAMAFFGRT9qmZVAjhoYXbXiTpRaXzl3NRc5z5utR/pvTz6DyLM8wKx5X6ypT6mEzlbDqbb9N7blN+7XGldLHcmrwb+9sfd+w67rzx9z0/dXRN/Lhsy/etSeeK8kZapGPV6LE1QotWBrGaHi698K6vZmO/11y9XuZUUwxhzNf+4oowHOoLY6ilFSWYEq+ZpTKzI0knIYSw2f7yQEn/L4Tw2+ed69p4AwAAAGBeV/vUTPFvamb2xZL+VNKrN1//oqTPkvQsM/ua8853XdSY2Vdnfv74PdYKAAAAALt8paQPkPT/SfoOSR8fQnik1hWZv+C8k72XYU+U9K07fv71kr7NOcb+gs4Pi5eqn9WkqSVWx9GBq2t95xSqlZWqkOWqpJXS3upSx8ZN9fKmlZXW7k2dqzmu3Itn9wuuqmeNtPUOOzme8FMP71RzReW9aW+1mQw12RBztUZokc7WIvtj6BhjpzTlHotSZk3r/ji1c41pzHkt+b77amiHWAntvLlrjFlpbaqxzxt/6Fx9O9TqZ5IuhxDeLOnNZvbyEMKbJCmE8A4zO7cNZPFPHzN78ObLlZn9bW3/int/SW+tXDQAAAAAXHVHM/sorTPJrt18bZt/dzjv5PM+z71aMu0Okr4/+nmQ9AZJX773cvcR96nxFgrIfTrojOis0ovf6DhvX5m5lCJE/g3w+U3+3uO8EZgT7Y5gpbe1L2Tgi+7kxj5v/Oy8R75PlRbds2bKCFHrvjXeyMLQKFDpvCmboHsjKy0+NO6x18tcSh8a10Sjxpy3S1O+Sca0z7y99bCZqz9Oasrwej/We2oW/HdC2Rt0mgEWf331+6LinyAhhPtIkpn9QAjh3Fw2AAAAANhXCOFBQ873Nt+8/YLGzC4lt+UrzgIAAADAOczs75duDyH8ROl210WNmX20pKdI+gid5rSZ1gli08fATjJfp2rS1BJHJ6fXbOfuULp6jntjf76vTCm1LXebezN7IU2ttMm/psdMKa2slAZWk6a2fb7vuPS58pyza3zPOV5xzxpp+2Vc/gQhmvs43eHr0OKdPOZ+1hZzTZnl0HqTf49FA+ZKaSqNnVt7bX+c1nO1MOa8pfsb/1rpvmhAa72ns6VaF0Oowcb+sRxw+tmnb/77XpI+XtIvbb7/25L+j6ThFzWSniHppyT9I0nv2H+NAAAAALBbCOEfSpKZ/Zyk+4UQXr/5/n0kPf28870XNe8n6etCCBfusxkAAACgB0HmzsxZsHtdvaDZ+BNJ73veSd6LmudI+iRJz69YWL1c9bNY+vNcypkzTc0Kj8jqZHuQo9Xp997UtCltp7P5Kn61qDRWU12spCZNrTSGvxLcsHMkbbW3nfSXUE1LnB571rRIn6oZb+ysid7baZQMzVZp3VdmybzV6Vqn77XIkOomFe1Q3nRLShebMo/Xq8UvbXTkF83s+ZKeufn+cyT9wnknZf/0MbMf1OmvquskPcfMfl1JSTWqogEAAADjW5d0nrCZ94TMzMLa4zZFA/7W5qYbQwjPiY/ZdX7pUXl58v1Lhi8XAAAAAM74ZTP7cUk/ual09hOSZGbXmtmDJX2hpF9WZn9N9qImhPAN7dfaSKmqWe64yjFW3uMyqV7r73dXKytVIStVScvd5m0QWarOVpPC5a00Vhqjfu2nx3nT3s6kiEVy55XO8X5icnTkyyE5OT6QXNmxG3G2qIyWyzyoacRZmrd1o8sxGmcOTXeaK5Wq9Fi0bvrZeq4WxpzX+zrrRg8pXFPqpSJbj00/D+U53u2Aq589TOuiZM80s/tIeovWVZdXkn5O0neEEH47d7K3pPODMzfdKumWEMKr91kxAAAAAFwVQniXpO+W9N1mdo2k6yW9M4TwFs/53s9Snybpr22+/jNJf3Xz9Z9Kem8z+z1Jjwwh/JF34W4nyX9T3kIB6XG52wrHrY6TyMqq7yvlXITIG0mp7TET80ZW0jXFc3v7xZQiP9trGF4oIbeGfebKjleI6EzWsyYZokqLD8da96mp3UtaU3ig943t3g9vazf2e83V66VHudfM2BGToa/V9FfMwdRJ7aFowHnH1hgzojXV2OeNP3Sufq331PT992cLIYTbJL3+3AMjl84/RNL6ouY7Jd0thPDXJN1N0ndIeurm65u0vrICAAAAgEl5IzVfIel9QgjHkhRCeKeZfb2k14UQvsnMvkrSLWMtEgAAALjoLkifmirei5q3S/pYSb8R/exjJL1j83U5E6aFXIpYunn/JPN1abxSbxp3oYDdqV7r4fOb/mvkUsTSeb0ven+q1/CeMLm50o34x5nbxl6fZ6372LpfS+pZI02XMlVqE1D7dhnap6Y23acmy2Gu9hklLTb2j5lKVXoscmsfoz9O67mGGnveMQs+NNciv7KH8dJje+hh02OPmR5zfDE17587T5T0c2b2XEmvkXRPSZ8u6cs3t/8dSc9uvzwAAAAAVx1qn5qhXI9KCOEHzOxmSZ+pdcGAl0n6uBDCSza3/7Sknx5tlQAAAACQ4b7U21zATNuAM+g0CplL/fKmmJWqn+XOSY47OtnOsjs5iSavyCYq9YvxViHzKvXRyaVCte4dk85VSgPLVSHzrr32uNxjW9PbpsTbs0ZacN8a72+XMaqk1YxZk8lQO683jceb8dFivN4zXA6rgNFa6fVTk/Y29rxecTW0biqhXbRUpdZ5rTV6rFa2/LKKF6X6WY3snx1mdmMI4bGbr39QmV9NIYQvGGltAAAAAHCu0mepr4y+fvnYCwEAAACAGtmLmhDCN0dff8M0y0kXodPIYCmVTJnbSs03TzK3JWlp5gztlyqc5VPJrvUNXjmvt5FkbozWFc5qlcaru4/547xNRXNjp5rc/0yqWvqSzpYgPK7cUFiqSuYxdtZAzVytx2jdM26uQkwl3mplY6c05ZQei1KaVk3Tz9ZzjW3ovLX3d9HmKkdY05hzrnSxHquVtUh1WwbSz/K8zTdlZg81s6eZ2U9tvr/BzB483tIAAAAA4HyuS1Qz+3KtG3B+n6TP2vz4nZK+U9LHj7O0glyURcpHZ9LCAN6ITmSV3LaKf7DaP+qSbj7P9ZwpFRQYWjSgNEZpfd4eMylvAQDvmrbX4e234zuuRQQmV2Bg0k9ZjtLtcLbzsDPm2j+ZixC16FkzZX+OKTf5Dx3vvDE9Y7R+bOfq9TKn3P0aO2Iy9PEsRfPSXzddFA6YK8pSM15pzClDtiU99sc57MIQRGp280ZqvlLSQ0II36LTLJc/lPTBYywKAAAAALy8yYR30brppnT6Ocs1ki43XxEAAACAM4KsqpXEReC9qPlVSU+Q9E3Rz/6ZpF9uvqLUSfLf8+TSyrz9bAppaqv0NRQdW+ors5roxVeat3VqVqlQQO649NjSXN4N+7njWqSzleTSyry/aFaXkvML78TF9qlJjdm3ZuwiAt6sDu+8S8p+qc3cqEmlaiG39lKKVG1/nNZzDTX2vAfbw+aiGfMXwVRjnzf+0LmwRN4/M75c0k+Z2RdLuouZvVTSWyV92mgrAwAAAHC7dfWz5VVtm4LrUQkhvN7MPlbSAyS9r9apaL8ZQshWkQUAAACAKbgv9UIIQdILN//mVepZMzTlbI/qMUcnp9d03s1F/r4ycTqbr0qaO/Wp0EenpnLZPsfVpIuNOW9qaFrZ2Up1wz9NyfWpScUv6eKnDd6+Na0/CBqaalN6CmrHrknJGVoJrTSXtyeM1z7jDa2u1ronTi+9XuZSk/Y29rxei+phs4QmUr31sOmxx8xF+KVwiupnuxX/bDGzX9M5Wa8hhE9ouiIAAAAA2MN5n8V+X/S1SXqKpC8bbzmJoNOLb29fmdynQg0iOlZ4tFYnp4McrfJ9ZcZU6mdTF+1o2zsmvc0bWfGOt32+b95UbrzaCEy6jn3nHcVW35pCz5q5PvjKPbQtNqynxtxn2uN+2ZoxWmzsHzPqULofpbGH9scZe64Wxpz3YIsGtK664dX6F8Zc9yM39j7j9xgV6sd6Tw2Rml2Kf5mFEJ4Rf29m35b+DAAAAADmRPkEAAAAYAHoU5O3nIuaXHGAtK9M7pxC/5nizwvjr7zriM9x9rPJFQOQ8iliteHImp4ztT1mvHO1Hq/9cbtfQNXPQdy35lB71ozZp6Y015ipWa3nbTH2mD1mUmNu7J+r18uccverNu1tzHm9Whe/WJweeqTMVQyhNH6PqW5YuvMKBTw4Pd7M/raiTNgQwi+NsTAAAAAA2+hTs9t5j8rTku//TNL3R98HSe/fdEUAAAAAsIfzCgXcZ6qF7F6ATiOIufSuNJTtrZKWu62UplYYY3UcpYut0hSp+MRrNZVcCluLCmLbx/l6zKxv2/2S885VGq+mF433uJpUuVQ3PWtqUthafCg0ZmWn2rly96umZ42UT62prcDm7ffRIl3Ma8xqZS3kHotSxkxtGl3rucbUYt6a++uVFmLsohpa6wpiNeOVxhy7kteUOa+txz7UHFWUEL8CAAAAFoCSznmX5l4AAAAAAAzRd6QmTj+Lw+XOZpnFtLKaJp2JodXP/M0yt4/LVSFLq6R5S/5tj+dLzappnFmat0WVtNz6dq1x3+NapJWVGnFO23wzvi/xvAtqxClNm/pU0xC0JvthziJFY1Yr8+qxgeVcWqe9tZjXq+Z5THXXmHPOpo1zNeasMWbTzx6rrk2PSE0ekRoAAAAAi9Z3pCbHuXl/ize6U4roFOY6Ojndjn1ysj3Z0er0+8uFZYypHCHyRjF2R0+8m/zTY70FBUpRm1K/nDGPG3rO2TGi+59+1BA9PSfHy3zLnjF2z5qhBQBqP/2v2YzdepN/zXilMVv0SKmJOrRQGju3dm9/nHTM1nO1MOa8LZ63uYomjKqXTf5eY0ZWvFr0mJnr8ZsHzTd3I1IDAAAAYNEO5GNfAAAA4LCt99Tw5/su/T8qV0PS3k358XE1KWd7hMAtOtZbNCC3KX/9va+vzCqTtpUWFCht5vesr3aTf+649NiatLLa8XJr8h6X8vbsibUIF6+O9g/7p2ds9a3x9qxp/ZuiRfZCmr0RmyqdZux9qz20yZgrtWiMvjK9qymG0GNWUKx1D5suigZI/b85x0zNGjvFrvcXNXrV/0UNAAAAAKqfFbCnBgAAAMCiLSdSk6t4Vqp+VnNcaV7nhfHqOEkXW81/RV3qZ9O+glg+vSuXElebVlYzXm6tpeNK53nPSSu85fTRs0bayvnosU9NzZpajFeTGeHNeJgzM6J1dTXP+ekYc2Wd1PZVya23RcW41lr0jslp8boduxLcQWqdBjZ2Rbap+uPsM/5y/gyOEanZjUgNAAAAgEXr+xI16PSDB2//mdynO+kHGLmN/elxq8xxybGr0nFbw+UjJrlN/6V+MduFB/IFBWoiEN4ohrfHzPq28T5dKEWSclGS2uM855x3Xs6ZYgPxRw+H0rNm7D41ubla9L1p3bNm6Nil8Vt8gt7iw+AW/UiG9pVZ2n7g1hGiMefdx9AP6y35vovCAWNu8j9vzDGNWVlk7Kolh9vDhj01eZNEaszsOjN7mpm92szeama/Y2afMsXcAAAAAA7bVB/1Hkl6jaRPlPTHkj5V0o+a2V8PIbxqojUAAAAAi7VOYiJSs8skFzUhhLdLelL0o582s1dK+hhJr8qfqNNoo7f/TKzUO+Yk83WpoIBz3qOTrU4gOjmJG9r4xhhTmt7k7WeTS4/z9pg5uw5fulhNOttcx5133lVz9axZz33K3bOmxz41sRY9a1oUEVhStkaLDJcxU/vGLpqQm9ebRif5U91q5mphzHlr7u8+vCmL3fWw8T64+7zAl9rDppceM4ebitYDM3uYpP+s9bv2+0II35I57jMlPVvSx4YQbh5jLbMUCjCzu0v6IEkvnmN+AAAAAPXMbCXpKZI+RdL9JD3KzO6347i7SPoKSS8ccz2T7zQ2s2sk/bCkZ4QQ/nDH7Y+V9FhJet+7TLw4AAAAoFtWzKqZ2AMkvTyE8ApJMrNnSXqEpJckx/1bSf9e0r8YczGTPipmdknSD0q6LOlxu44JIdwo6UZJuuHudn4w2VvVrBS+9qapFc4z5yOZS+GS8pXMSn1l4ttKVdK8KWa59azPG97rJdffpTRXKU3N2y9mzONiLXrWzFbV5ChdU7SO47Tk0ETGrpLWoueMZx21PTi862tdaa1FBayaamVeNT1X5uwB5NVbhbcWz1WLnj0tquddOK1T0WpSuFr30SmN3+Mb+kK4h9Z75q+6RdID4wPM7KMl3SuE8DNmdhgXNWZmkp4m6e6SPjWEQGIjAAAA4DRxSefrzSze/3LjJvjgsglmfJukx7Re2C5TRmq+R9KHSnpICOGdE84LAAAAYD9vCiHcULj9tZLuFX1/z83PrrqLpA+X9Cvr2IbeW9JzzezhYxQLmOSixszeT9KXSLpV0hs2d0ySviSE8MPZE4NOQ825CmWFhpjFaKS3+WbunF1zb6yS41bxD1bXFiaYRqlJZ4vKZdvH5dPKSilxNWlqdVXchh+XO2ef82K51DZJW6U9Fl3Sccrmm6WHqSblbMyCO62zNcYosJTTutHnlNkkpXlbp7ot7T7uO/au8T1aVGTrrhKatKzKZd4nYeyyfT3cx7511HzzJkn3NbP7aH0x80hJj756YwjhLyRdf/V7M/sVSV89VvWzqUo6v1pn+wADAAAAWKAQwrGZPU7S87X+mOf7QwgvNrMnS7o5hPDcKdfTTfmEc+UiMM7oSfE4b+THGxXKrUH5YgDr73dHHS7nh0vGSwsP7C4o4P2E/+x4vuiJNwKTKwaQ3tZ6vO3z647znFM6r8WnLEdnNvbvL9uz5ox4vQ0+n2i92bf0m2zKT56HFh4onTNm0YDSmGNv7Pca2lemtuDBXOYqhlAz7z6GPu7e12P6a6qLyE1N1EaaNmRbo7cGXbXjL0OQdZWpEUJ4nqTnJT97YubYB425lln61AAAAABAK8uJ1AAAAAAXWOirT01X+n9UrkYQCyldW04yX+fGTcf2nlNYU1oo4OjoNLGnlEqWs096V414/Nab6EvpXd6eMF6l8XJpaqUxvCliQ885T0cbAudV0x+mdszanjXe81r3lZlrL3JpvKHpcnP1lfH2VZGGp7qNfR97m3cfY+4VP1hLKkowdv5n7/mlGEP/FzUAAAAAJPFhZw57agAAAAAsWt+RmqDTqGGu0pg3XayUOlaTppae561+dhKleq3Snib5SmYe5TS13Slm69t8L4OaFK7WaWDeamWl++hNeys9B7nHrOac82THTD6S6Kkayl7G7llTKm7jfZsNTVPrpUhRSevqajkt2kTU9FwpVc3qJVOldYW3ocaet0UWlHcd3fWw2eeN0MML1JuK1kODrtL4y09FW++pWej/70dGpAYAAADAovUdqfFIL7qdEZPsJzq1EZ2IJcetvGvKKPWziW9rceVe6qNT0+vFGxWqjaz4Iz+7H6d03u017D9eSW2vG6+hfWvS1eX71qRrHdi3psWny62LCLToU+M9v+YT77l6zEjjRpl67CtzKFGhFkUdcrz3tzT+2K+f7vVY7WOoFmFZ7/ito0L9CjKdXCFSswuRGgAAAACLxkUNAAAAgEXrO/0sLhTg7T8TKxUKyB2Xpod5z3NGAlfHpyetVmkqVTzgtb4BS3NlUslWZ1K4fKlU2+ON2xPGmy6WS1Nr0Yum5rhUi741NNnaGKNPTe7pGbtPzdCsidZZJ+eNmdOi387QVhjevjLeDeZTZqeU5mqd9tZCi7YlQ7OdWmQ0dVc0oFbrDfZjb9gfMyVu7FS3jgTp+Jj0s12I1AAAAABYND4CBgAAABYgBNPJMX++79L3oxJ0Gj7PpZJ5q5+lYfjccaXqZ+ltK99xq9xxBXE60tmUq923nZw5bv/wZHne/SuXnR1/d1pZmn51nLnNm6ZWGm/7/LbHlc6r7SNTqpoWW2zdeu+yW6QBjd0jxXNO7Xmlc1r3mGlREGmqilpzVRBL527RS6W1mhS7Fo/tlD1sPGsorSMt3thFOlovqVStc17HXEOL8bF0PJsAAADAAqwjNQv9EHNk7KkBAAAAsGiHF6nxVknLpbCl6WHe0LnzuKOT05aGJyfbJx2tTr+/7JzWq5Smtn2bL3UsrqC2T2Uwb1pZbk2tx0vVHBcrPRa1jT69Vpei8Ru8s+O3Rb4Rp7Sd51HRiHOMzIqaJpie82vH8J7jTZkZo0jR0EyTFmsfs7pW6bGYsulnzVxjP2Y5cz1X+/Cug8acFePV8I49duPMMe/jzIKI1GQQqQEAAACwaP1Hao6T/5a+9oxzVW7Dvje6I7k/DbaKRznXE2b9/e6ISbqh3BtN8axB8m+iL/W98UZChkrXdJJ5nMpRJt9x3nn3OS/nQves2eeuj9lzpaRmY3+sxYewNUUDpOFRoZIxN59P2VcmVRMVmsuY0ShvFGzX3B5TBiC672EzV9RmzA37Y79hDitqE4Lp+DYiNbsQqQEAAACwaBf4Y18AAABgSUxXTvjzfZflPyql/jOlAgDxbd6Us1K0rzRXPER03GqVpIutpgknltLU/KlZ+U35ueNKx9ami/mLHOzuF1O7vlip/0zuPHrWRNLMg5wWGQ+l33i1e1VjuTXWpki17ivT437jMVO4SvejpuBBbX+XoXOVUp+nTHvrIY2wRbqh933VfSpaa94X0BiVSnLntU6J6zE3FK0s/6IGAAAAuAiCJKqf7cSeGgAAAACL1nekJmh39TNv9ZRSlbTrnMfFF8OlFLbceKXjCnIVzqR8z5m0D0p8Wy79qryGun42NX1v0nSpmnQxb+pcaX25dLEW9zd3ztm5LljPmjGqVQ3tU1Mab8w+Na2zP/bROtVtzGplU/aVWbLWaW9D593HmBXZYi2qs81mzl8YufHn6h1Tex+9+c8dCUakJoNIDQAAAIBF6z9Sc/VTk/jTk1JkxRsVyX0aU4roeAsKOAsFHB1tf+Z9chJNMOFF+CoT7fFGWdJIkrdwgDcC442s1BUAOB71OM85Z8eoK0pw4dRs2C8pfWDn/fR2zIhOTR+d1j1mSmOMvbF/qNL9KH2AXBMVaj1XbYGCocaed8reUEODBGngucvCAT1WAhk6du0aCNleRH1f1AAAAABYC5KOnendFwzpZwAAAAAW7fAiNbmCArWpYzXnlXrnOAsF5IoBSNvpSaU0sNxtZwsK+F4G3mIDufWt59o/rSxXDKDFeKW1tzhu6DmpUoGB2KL61KTG7FvTOmWtNr1rzOyPmrH3Gb9mrhYb++fa9H4oalPsPLzpdrvm3nf80vM4ZUrcwfSwmavJVY3aF9MB/iI4kLvRGpEaAAAAAIt2eJEaAAAA4BDF7U6wpe+LmlyfGm//mZi3Spo33Wyf8yIWFzhLxlvFP1hd6xuwgVyamr/il6/SWHqsN62sRmm8mjS6Fsflzkl50wHd46fx2IHDp2+DfN+a9LGYsG9N6z41uae1Jt2sdN7Y6WxjVlpLDS2cNFdrjVKGS22q29C5WlRd8xq7B1BNGmFuDbXr8L42W7wPZjNm5TKvsUsn9nAf0ZO+L2oAAAAArBGpyWJPDQAAAIBFW36kJg0J55p0lqqOla54S2Pkzkt/XpFJVaoglqtkllZJy92Wpj7VpEx5m3SWKq21SO8aWq3MWwmu9rjtc7yPs+8jmAvfiHPsSmaxmiI7sdYVm8ZI1ZmquJF37SWtG1geqpq0tzHn3ceYFdla674SmvcJHztdbMyyj63vY8eCyLbLIFIDAAAAYNH6jtR4CgV4eQsFlIoBeG9LP4nMHLdKjjs6Ot1yfVnzyEVjpHx0ohRJSqMT8bGtIys1G/bPzutd3+4Xofc+7XNezsH2rPH+VmqxcXfoJ8pjb+yPtY6keNfeIirUYu1jtpoojd06KlQz11y9eMaet8U+76Hr8H7A730fpPVQuo/ctPhlMuaG/bGLAfT9Z/BOQQssXDENIjUAAAAAFm2Bl6gAAADABbXArUBT6P+i5iT5b/p1Ka2stMk/N17KO0ZJRZhwdRKlga3SlKvdm/5LaWC5XjSSf9P/9tj7b/IvHetNK/Oms3nHSx1vPWa+IgTb6xlehCE1tG/NmbHj+GyD3wDxqy7fs2a9klOFnjVjphbVzpWmqFzlfW+Pnabm3VTdOpukhw3hrXu9LFnrtLfW8+6a26NFv5jWr/1F97AZ05i/PPYpUEAPm4uo/4saAAAAAPSpKWBPDQAAAIBFO+xITXwl600xK1U4847hTHex5LiVt6/OiEppakMrl+1zXG6uUnpXzXipXCpebXpYTWpfqqbS2oXgvftTVjXLjVGTblY6b4yWDHOlunnOT8eYqhJaOn7rVLfainFjpj6NPe/Q3jktKrJNmXq4qB42Y+eaesYeY3xS0S6Kw76oAQAAAA4F6WdZpJ8BAAAAWLS+IzWe5pveppop73G1ldFqjousjqM0sFU+lSpOETtb8Wt3I82T5LiappU1TTrPriOf3lXbPNMznj89bv9GnDUNO887Lz/eBW++2SKLIDeX91Ow0kO7T+PLfc+Z8lO61qlutSlcXr01sBxb67S3FnpozNniPrauhLaoVDRp3Dd7yZhNP2vX1BEiNVlEagAAAAAs2uFFarz9bK7L3Fa6+q0dw3ncVkBmpkIBsTQS4N30Xio2sL3pf3jhgaEb8dP7mFtH6+POOy/WOmq1pceeNT32qYnVfDg4dp+aWM1G/n3GHzpX7fPbY9ShdVRo6Cb61saOqvXQ56h10KH03B+sMSMrXrVvklwTso4RqckiUgMAAABg0fqO1AAAAAA4RaRmp4tzUeMtKJCGikub/Ct603gf8aOT0+Sdk5MkDWy1e9N/mmaUSwNLN6znig1407n22eTvLUrgLSjgTWfzp8751ldzXGyfVLm0EEPO4goCtFS7YT+nRcramH1qUq1bTYyZLpaqSeHyqum5MnY625hap721nrd2/BZZS731sEmybvsvHDBmutgY/XHoTXMRXZyLGgAAAGDJgrhOy2BPDQAAAIBF6ztSE1d4yFU1K6WLte5FUwrZ584pzZ0cZ30/G1upVDWVy0rH1qaV5dLAaseLeVPW6lL22vasWY95IGlqNe+DFikpsSkrMY2ZprZPCtdUBYxapCONmUrlnTcdv3WqW03VtRbGnndoFbsWFdmm7KNzkD1sWqeLte6PUxr/AEIcQRekqt7+iNQAAAAAWLTOYwMadjXq7WeTO0cqFxHInZdGZnL9cgrRo1V03Gq1vajVatgn7enG81yxgTSa4I2ylCIX8bHe47wRmNbjbZ/vfSyGR2DG7FlTHD/9iGPgb4f0kcj3rUnu03G6g3ag1n1qSuO1bphdE0mq+UC1NGaLqFCLKNDQfiS1n/BPtXG8ZMoIUQs99LBpUWiidfSyxePShbEjK149vDlnQJ+aLCI1AAAAABat/0gNAAAAACI1Bcu5qMmlknn7z3jHK/WiKY1xXebnJbVrj+R60UhpOlb89fCN6P6+L3VpW960spp5vWlq+xRAuKpFb5uSUs+a7gsAjCnNhoi1SPOoyXKo6Z1Tm63hXV/rjdRT9g/xnJ+OMXZ2Sm78paW61Wze7+G5b7GO1mmYLSyih02sdRGBmie1RaUJLB3pZwAAAAAWjYsaAAAAYAmupp9N8c/BzB5mZi81s5eb2RN23P5PzOz3zex3zOzXzex+dXf8fH2nn8V5gzXpZ6V+NrlzStXPvLeVUthWhePiw6Jzjo6260adnER3pkHGUS6F7WyVNG/qly/NqnRcTRpYTfpVKU2tVIUs91h4H7Mx0sj8fWr6fttv8S61RSpIiyppQ7MmWvep2SfToqYiW4tUt5wW/Ui84+fGTsefMtWtphfP2I9Zjnfe2rlbPLZT9bDxvuf2eVy672FTY8oc15JS7jLOY2YrSU+R9FBJt0i6ycyeG0J4SXTYj4QQnro5/uGSvk3Sw8ZYz4L+ugEAAAAuuH4KBTxA0stDCK+QJDN7lqRHSLr9oiaE8JfR8XfSiJfmXNQAAAAA2Nc9JL0m+v4WSQ9MDzKzfyrp8ZKulfTgsRbT90VNy7J1tZXGahpuesPr6XEV1c9yFc6kfCPNtEpaTSWumiad6Xm5imTr23a/NL3pYmOns8XraHFcbg2pFhXOSg1Ct8Q77hr8pohnzTfilLbyMpbWiLN1tbKac2rSw6T8763WqW5jp3DVVPIaW02q21xq0t72MbQhaIuKbFM2B229pu5T0cZMFxsjn3aBpi3pfL2Z3Rx9f2MI4cZ9BwkhPEXSU8zs0ZK+XtIXtlpgrO+LGgAAAABzeFMI4YbC7a+VdK/o+3tufpbzLEnf02JhuyznosZbACD+viYa443MpGN6z3M+4hbXAkjmXcU/WF3rG9BpOwKTRn680Ynd/WzW5x3tPM47njcC453XW8igTXQn/yLxRstqihdcCN673uJT2VhtzxrvOlpHZ1qbqmiANN0HwOnYNX1laqMJNVGM0nMwZoSoZt5W43u0aEfS+v039p737rQI33rHv0BRm76ab94k6b5mdh+tL2YeKenR8QFmdt8Qwh9tvv27kv5II7nAfwUBAAAAqBFCODazx0l6vtYfl3x/COHFZvZkSTeHEJ4r6XFm9hCtrzzfrJFSzyQuagAAAIBlCOoq0hdCeJ6k5yU/e2L09VdMtZb+L2pOkv9K/j413uNKPy+lsOVC9qU0tdJxFXvASxv2cz1n0oIC27ft34tmn342QzfY16az1Yy3ff72cbl0Me9xqRZ9a9wFALbG7vxXwFG8E7ZQNKCXPjW5MWp61pTmXUK2xtC5vPfR21dmjE3vh2Lo5v3W8+5j6PPYumBGC941pb8SuywcEOshXWzOiiEYW+d/0QAAAACQtL547aFSYocunX8IAAAAAPTr4kZqaqqklaKUpTFyaXDOuVZJKPro6LTLx+XCEDVK/WyGpqlJdWlb3upiXjVpainvY5FLK9vnfuQqnl2InjXHw+/jltJwU/apya2jdYWzfdbnTQWaKt2nZK5WGK1T3bxzjV11bSjvvKW5ax9br6l62LTu6yQdUA+bGjWl5aT6nN+FIGtuJyI1AAAAABat70vUuBZ3TQGAXG+bkn0KBeRuK31KV5qr4tlYnUQb9lfppvfdPWdKBQW8G9u3zx/ezyY9zhsJyc01ZYTIe39L89ZEXehZUzB0w753vJoN+vusY2h0psUHm1N2ep+rY/1FKxrQOkJUO/fQ3jHpOqbsFzNXD5uDidrUPAn7hJ4PuClQX31qukKkBgAAAMCicVEDAAAAYNH6zlHJpZ/V8KaplXrRlEK9NcUGnMfZTM/S2bSy3Sls3hQzKZ9W5k3Hqk0Xq9nY32J9ubXuc15NKpm3AEBNuuGkjuLHJV1r4741LfrU5MYbY2N/zVze46YqGtBirtrUPq9SFkvrVLfcXD324hl73qk27LdYQ+v334Uz55t4gUg/yyJSAwAAAGDR+o7UAAAAAFgLOoiA0xiWeVFTqmrmTRfzjpc7Lv0+TiVLxxh6XGK11cMm6iuTNLTJ9ZxJe6fEVdJa9KIZWrlsn+Nq0sVazJvvHbN/att5cqlkLfrU5ProFKXx3YG/RdJ7t9W3pnWfmjTLIdYixSVW07PGu4YWFclSrSuUDU21GTt9qKbXy5K1TnurmTede8oqZDkt+ui01npNaaZul9XQYmOWSxw71Q1zWuZFDQAAAHDRBI17Ub1g7KkBAAAAsGh9R2riq9FcWpm3qtk+TTW9x+XS1tK5So05PfOWxpvQamCaWjqGtxpYrsLZem5fWlnNeLnzy8cdVx3nOee8NcW6r2o2tqHNMr1j11Y1i3mzK2It0r68aVbeFBfveLWNH3NzeVOpWmjdtLI2Naum6loLY8479utiqsacc6aGHkxjzlhNKppUn462EIeSFtsYkRoAAAAAi9Z3pEZq16cm5Y38lKIsudtKEZ3SeKvMcelh0XlHR6fbqk9Okg37qziaEveYSfvP7C4o4I06nFlfpp9NOsZ2tCcfCSlFhbyRn1iL8Uo9cWqOy51z3jpyagoAdB/dOUrvU7zeZCfs0N8XLQoA1ER0ajcjt96831vRgBZj9NjrZWytI0QtjPk8TrWGdB1T9rBp/fwsKmqzjyW/cc9Bn5osIjUAAAAAFq3/SA0AAAAA+tQULPOixlsAoHScZ+z0+zQlLBdyLqWpeUPl3pS4mXhTzErntU7byp1fGsOfHlfqtzOs+MF5cqlkLfrUeOY5VxzvbfAbJX5GRu1ZI+X3iNaWyxzaNsH7P6oxNiO3NjSdpkWBgrFTqWrWXpPq1mITfY3WhRFq5x5zw36LNbRORWthzNfFbGp7zBxwKhq2LPOiBgAAALho6FOTxZ4aAAAAAIvWd6QmrvAwNK0svarNjVFK7SpdGbdIU3M+GxaNF1dCW622B1ytdlcyWyW5EbkqZGk60klF6tjZMXbfydr+LjVV0urS2Xzr897fUqpXiz41se6rmo3B+5utJhNhrspoY1ca8543NK2lRVrQmNWrSuubM9XNo8cKb97Hc+wqZDkt+iu11npN3lTOpIhk/9XQxszd7RjVz7KI1AAAAABYtOVEarxy/We80vlqojO1EZ3ccR1+0F4TjSmNUbvBPheBKUVWasYrral83LHruNzYqTF71rQuPNDcmT41sWTtx+lHjnsqPRQ1kZXaMcbcgNz60+AWn8i3iHbUbLYf29CoUPqYzRUVGlrUoaRFFKxF8YKp+ujsEzBo/f7x/l5ZVA+bOd/g6EXfFzUAAAAATpF+thPpZwAAAAAWbbJIjZk9TtJjJP11Sc8MITxmrwFyaWXeAgDefjZpSlhpjOsKt+XGKJ1TkZoW1QLQ0dFWVw+dnEQpYqvdxQCkfM+ZtKBAi1Sq3BjeogSlVLeatLLa8Txjl45LedPKWhQYyPEWHigas2fNyQi/rnJDtk59qllDaR21e11bp+C06IUxdK4W+35r05Za95XJjVebmtU67c1rzB42LbKMpuyj0zqFdK6WK4tKRZP8T9ACE5Zovpk15bP5OknfKOmTJd1xwnkBAAAAHLDJLmpCCD8hSWZ2g6R7TjUvAAAAcBBovpnVd9wtfuJqetN41VZM86aV5W7zVkfq+1lyp5iVzqs9bmhKnHc8f7+dUnpY/oks9djxys3dfVWzsbWoQtZ6vJoUkppUrzkrLPWeZtRDX5nejd2XZ8zeOa0rsk1ZdayFHtc0G97sF0V3fy6b2WMlPVaS3pcyBgAAAMAazTezurtsCCHcGEK4IYRww3sObDUBAAAA4PB1F6mJBUm3ba5G00yE23mrmpWOK/FWP2vdcNN5nEXP4CpZ3yr+werawoCnttOgTly37VPxy5tmlUsDS8fLVQ2rbQi6vQZfmtr2PMMbcebGPu+8/HiH0XzzUvICv5I5bi26L0MbcSbDNW8sWFKTNVFTRal0rPe4Fk0HW1RdGzNtqXWzTO9cc1Vd8/LOu2tujxZphEOr57VuVlsy15q8z1X6K3UR1dCuOoAmnURqsqYs6Xy0mW8laWVmd5B0HELgqQEAAABQbcpIzddL+jfR958n6RskPcl1dk0EplQAIP6+NhqT+xTDO0aL45xyvWjW359+PNPi0/qaTe+lCMyYBQW8kZ/a43Lr9R533nnbYwx7OzfpUxNLk1sH/rY5vm3kSFJNvxjvGLU9a+bqP9Nb0YB95vKsIV0H+4hPjbl53ztv7fhjFy9oaZ8I1lzvW2+EenE9bBaOPjVZU5Z0fpK8FzAAAAAA4NT1nhoAAAAAkYMtvz1M1xc1IUjHVwsF5NLKWtunoEAubS1ND/Oud2DxglUazq5IU4uV0tTi21ZJHN2bIlbaOO9NYcsdV5vONnTeVDxXLmVt13pj/hS+/YsXXAhHcT5EoWhAi541Q8eo6Vkj1W3Kr11Hi838NeN55knnapFKVLPBvjbVrWYze+sCBS3U3EevFu+5Fq/NuVK9SEVrLFuGCgvU9UUNAAAAgA2qn2V116cGAAAAAPbRdaQmBOm2TZjzjrmDaquaeVPHcueUjvWmrJXSxbzHFRydnHbyODk5fWCOVmnlrTglzJcuVVKqtDa0GljpuJp0ttrxttfkO66mKtw+4+fPr8st6T1tLe5bU+xZc9zgfuQyFLwPbenp3qeXzFUtKo21SOOJeds/tOjBMWaVq7F7rvSoddrb0Hn3Gb/mecytYdc6PFqneh2KRfez6RiRmiwiNQAAAAAWretIzZaaCExJbgxvL5rSGGkkJRd1qS0okHksbORnM/7EfzvqUNdzxb/ZPh+paBFZ8ozXuv9MblN/6Zxd48da3H/vXFXij1AavFab962pjaZ4xhu7T83QzdclvWzMHjpX68hUqmZTuTcqNHaBgqG889bO3Xufo5rjSloXAvEeN2V0CxjBci5qAAAAgIuM5ptZpJ8BAAAAWLSuIzVxn5rbonDpNUNTzCR/DxfvGCeZr9PzcueU5q18lqJ91FpF36xWaQrX7p4zR2dSqfZfyNkx8j1ittc0XhpYi439Nf1nvGl5pR47JeljndN7AYBRHaU7VaOdrC3SRGI1KWfec2pTelr3uGiRFjTVhvDaNYzZc2UurdPeauduXYRh7OIFLefa53VLDxvEgpb1+2ZCRGoAAAAALFrXkRoAAAAAEUo679T1RU3QdtrZTt7+M6WqZrnKalI5TS0X/vOmqZXO8/bLWWV+ft4YA5V60cQpbN4Us1I/G2+VtJp0sdpqZTXrK1UT896v3Br24a1qVpNuOKWja+LndFu2b01tz5qafjHe8cZMU9unEtNcqSueec6bK2fsVKqc2v4uNalZNVXXWhh73jGr2LVOqyqZK9WLVDRcQH3/1QIAAABgjeabWeypAQAAALBo3Udqrl6MHkdXpdeU0spaTbjr+9KVsbf5Zunn12Vuq0wri6ufHR2dJuScnGw/aEer3Y000zQlb9Ww7XPqxvBWDcsdV5suNua8JS0e21ibRqQjfhTUoBHnSW0qWc6YzTdrx6tJDYl501NK47c+rjaVKjfXPtXKPGobSc6V6pabJ52rddpbizW1aAha04g0NmVz0Nr381J7knhfP5Z8TzpaGX1qsojUAAAAAFi07iM1V90WXeHfsfRpkbcAQC4Ssk9hgNx53ghMKaLjLRRQ+uRnxEIBsdpeNOUxdr80S5GQUu+YmshKTS+a0ry5eUrjnXdsTqlnzcXuU5M+LtFjcZx+XFihZmNxTUSndrP0XH1qxvxE3qtFhKi12ijGXP1xxty8XzPvPsZ8/eTmqZ3Lq/WapoxulVBEoIw+NVlEagAAAAAs2mIiNQAAAMCFRvWzrK4vauK9UMe5J9CbVlZ7nDfEV0odG9rPpjKtzKIx4qIBq9X2gKvV7nSsVRLb3t5gv3+KWe0YNWllrXnT4/xpdNvHedPbPGOfx9unpkWxganEPWuk7bfMVs+aFsUFSg+7Nw1jqj41+2z0nis1bWhq0Vwb0Uvjt+4r07pAwdiPWUnNfYz1UmhizNdt6qL1sImRioY9dH1RAwAAACBCpGYn9tQAAAAAWLSuIzW5Uty35XrWjMGbwpZLHSuNV0pTa3HcTEopbLkUMW8/mxZpYC3S2fz9dnzHlXrneMZOtUjFK1VQayr9aKXit9LietaMmXLmTQWZq0+NN82otueK11yVvJZkzr48Y/YbmmoN6Tp6TPXqfX3AHojUAAAAAEtw9RP/Kf45mNnDzOylZvZyM3vCjtsfb2YvMbPfM7NfNLP3q7vj5+s6UhOLozNx0YAzjbRrCgDkvj5PPGZNdKamCME+x20VBzj9+uhoa+u0Tk5OF3K02r2RX9qOYpSiMd4N5v5ox/4FBdLjaiI/LeatO254BKZUDKC2wMBB2upb06BnzZlfSBu1PWtaR3Tm+nR0yvUN3YjuHbs0vrevTG00aqoCBS3MFXHzahHRGTMqUjp27H4xOa3X5y1okv5apnDA7MxsJekpkh4q6RZJN5nZc0MIL4kO+21JN4QQ3mFmXyrpP0j6nDHWQ6QGAAAAWIKrzTen+He+B0h6eQjhFSGEy5KeJekRW8sN4ZdDCO/YfPsCSffc/077cFEDAAAAYF/3kPSa6PtbNj/L+ceSfnasxXSdhxL3F8pGamv7ynjHK6WwXTfwuNLccTg2LQDgPa4DZwsA5Dfpx7wb53NpZaU+MDVpat6+MrXH1aSV1faRKaW31aypB6uj/Bs/27NGqutb06KIQG6M2vNbp6e03jxcUlPYwNtzxWvsTe9L0jrtrWbedO4eNuy3TqWasiBFL2tqXUTgovawmbb55vVmdnP0/Y0hhBtrBjKzz5N0g6RPbLKyHbq+qAEAAAAwizeFEG4o3P5aSfeKvr/n5mdbzOwhkr5O0ieGEG5tu8RTXNQAAAAASzBtpOY8N0m6r5ndR+uLmUdKenR8gJl9lKTvlfSwEMKfjrmYri9qsn1qonDkHUuhyVJVs1y62D4pXDXVy0qpaLm5vfMU7qNFz/QqmXcV/2B17emXyQLjKmfb6WHbE3tTx0pjePvZeNPKcmtqPV7Kn0a3f7WyUh+ZFqljpTWNKt7p5/wN1b5PTZrLEOU5DE1vkuqroXnWUJsu1TrlbGjaVuuUJm8qVYvxW/d3mbLqWgtjpvPVPrYt15CuY+x+LIfSw6ZGTSqadLHS0WYUQjg2s8dJer7Wz9b3hxBebGZPlnRzCOG5kv6jpDtL+jEzk6Q/DiE8fIz1dH1RAwAAAGAj94n/TEIIz5P0vORnT4y+fshUa6H6GQAAAIBFW0ykJr4oPS6FN73NN2uO8zbYLKWVecfwVlhq/Ay2qK61PV4+hc3bqLLUpNOb3jVUqVqZv4mo77gW55VSx1o8rwcjVzWtNp0t9xKsqTomtW++GWtdEanFcUMroZ13m4e3gWXt+D1qnfY2dN7zbvMYO30x1kN1tlTvqWg1j9k+z+mhV0abKTu8d0RqAAAAACxa15GauMBD7kL+tuSTgGvG/OSsFMUpRWNyV9TeQgG1x61237ZKP+2o6G+Ti6Ssvz+dwBsVSMfwFgqoOa5F5CcX0WqxvpIWEZhSgYGaNfUo17cmfYtcORn4K7DFJ/c1n0jXztsiEjLmcSVDN1zPGXWo6e9SE8VosYm+hnfe2rnHLPjQOhJZUhMVKc3Vul/VXNHb2n5aXocYtTmU+9EYkRoAAAAAi8ZFDQAAAIBF6z79bGefmiiEmRYN2Ipi1hQA8KaOpbyh6FJPnBPncS167ESOTq6cLuHkdBFHqzRFKt+3xWs7bc1XlMCfEpZPK6tJF6vd2O+Zt3RcqiatrEXRhNn61MQqetZIY/etqehZk6ZXxGp61oyRpjbXhuGhqWml+zh22lvNBvsetE57a6HFY1bTs6e0jtZ9dHLrkcbvq9ODuYocxOhnc9CI1AAAAABYNC5qAAAAACxa1+lnsVImWfbAUlqZ5/zzJs6lfo19nLf6WS7UW5mmlpNW04rT1M7etvslVxrD28/Gm1ZWStNqPV6sNj2s5ry0mlwOPWsiuZ41Ul3fmhYpI0NTzrwpHlI+zWPKPjWxsSt51VQQa6EmRao2xa5FVbec2nnHrGLnNebrJzXmc5DqvYdNzTk1a5Xq0tFIRVs8IjUAAAAAFo2LGgAAAACL1nX6mav5ZhJivKM35OitNOYdozRvLoWt9Oi3qJKWYclaV1uNOY+jr9MKYtFtUay3RWUwb4pZqUln6+pfJbm1l9K+ao47O2/deTmlRpxLbr4ZyzXilLbfPlWNOL3v4ZLSw+xN4fLO6035WGoltFZj5EzZLLNGbYPRmrS3Fmoqy6WGPra1z6lnDfuY6z3XQyqa1z7vlzGbzc4uVxsYRGoAAAAALFr3kZqdfWqir9M+NVu8m/xrjispbez3RnS858TS4+LxJvzQPRfRSW/zRiS8UZya41pEfmp60ezTs8YbMcn1lamNWrXuUzP0fpz5CMZ5t5r0rNmK9kTjHadND5xqNg+P3admyg3NsaGfKHs3CPe42b6kRRSjtZq+PC2iUS3u79Dncew+OrEWr+ma41r32Gm9vtSY/Wy6FucxIUakBgAAAMCidR2pAQAAAHAVe2pyur6oiQNscaCt9FTeFh14TevoXCm9y7uxP3d+ep43Ta0msyaZN64HcHR05XQJJ9uLOFrF6Vjx5v3t47y9T1YVY3j73pTSykppYDVpajXr8x533no9WhcXqOVNZxu7yEPOpahIxpX0xqF9as6MVzHGmOfsY6rN0y3SfcZ+LKbqKzNlgYKxU3WGphGW9LBhv3WqV4s11R43VxGBuYp9YPG6vqgBAAAAcBV7anLYUwMAAABg0RYTqcn2qUkuVuNqaFvRXW9VszQU6Q1NequkldLUcueV0tRKx8Vh1n367zSUphzV9JKpqXDmTSsrpYHVjHd2TW2rvbXuK1NKCZsrDay1Up+a2PFtLaqkxQNWnF+bGjFmZbQpKx21TospmauS15K0Tnurnbumd05tzx7PGvYxV6rXXHpIlZP87814vYsJfrCnJodIDQAAAIBF6/qj2Ny1qLt1jDcCE3+9T5+aUtTFc1wpAuON6HifwcJ9tGiMaK+0VqvtA1er3Rvn0140Q6Mx+4yRi3DssxHfwxvRad1T57xjS+ddVVNcYD3esI+tWkd6zty/+COZwlQnx41/zeV61kj+vjW5JdX0rJHaFxHwfmo85cZf7xhDP+GvVdNXpjaaMFWBghZaREy84w/tHVO7jh42wNfONWV01LOG0jqW0HdrEkRqcojUAAAAAFg0LmoAAAAALFrX6WdSRZ+aKBR/xyk30eXS2VKt+9mUjuvw2d3uTbP/Jvo01S1OrWqRBuZdk2fsdH01x+061nNOrHVxAa+h6WupKXvqXEpSL7f61rToWTNmz5Xa/jhD07tqx+s9fWiutK25tE57q5k3nXvKDfs5rQtmlHhf01MW8Yi1Xl+tHlLnZrOYqgaTIlIDAAAAYNE6/CwfAAAAwFkUCshZzEVNtk9N8v1xLiLnrWpWW/3MO1ecOtYiTa2mAlsaEt6qeHb69dHRVtKNTk5OJz5axSlc23ckl2K2Pnb/Sl6lNKuhVdJKaWAt0tS866upGNeiwlupT01t1bSejd6z5ihE30SV0LyZAmlaR6xFqlfunNJ53lST1ikuLfrZ1FQka6F1f5fa1KyaqmstjNnDpvax9a5hqn4xY1Ty6qFHzJTry51TOu+i9Zq6YBZzUQMAAABcbEHsqdmNPTUAAAAAFq3rSE2cNZjLAitWQosOvGaMi9qaNLCalDNvaLu2OehMtlOufA0nvY0pa9PKcmlgteNtr2n/am/nHVs6b9/zU6WqaTlTVijbek7Sj2cyv9nGbcSp4ZXRWqda1M611Epo0vBKR960t/S2JVdYap32NnTedO7Wz6PXlM1Be0/16n19tefF611M8IM9NTlEagAAAAAs2mIiNTFvdCYuGnBm/623UIC3/0xu7F3f535+XeG23Hnxpx3eQgGlT7AaRHTiT9DPbmbfv5fM0GIA+4zh5Y3oDI047TrWc47n/PUYbX8FlOZqbcyo0NE1aWTuVFXPmtreMbGaT8b3+bR6zI3zJVNFcVpsMK9V09+lJoox1330zls7d03vHK8WG8zHjIqcd2zP5uq3UzpvqY/lFvbU5BCpAQAAALBoXUdqAAAAAFzFnpqcri9q4gBb7unzZnq508rSsGdND5vavjK520pparl5zpsrw6IxVsm8q+gHq1W8Ub5uY/vW2I372bRIA6vZ2H+2n42v3473uNw5+5y3PUY+hD3lpv+55PrWnNRs+E/FPWuOLX9cSc1G5bFTXLxrGpry0aKfzdgbvefaYO81VdpbCy1Srlr0JRozzTE3T+1cc6V3jZ0eN3YRARysri9qAAAAAFzFnpoc9tQAAAAAWLTFRGqq+tREYcs7jh3mjxdVWyXN25umVMksJ5cCl87bWJoiFac7raIYszc1q5Sm1qJK2tB0thLv+kr9YUrz5s6rTSOrqWQ2V8rambXGH9cU3iOD+9Zk0tckTVsZbczUGm+qSYsUlyn72dSkZo0td7967I9TW5Fs6HPnTbkqmbIf0tj9fGKH0sMmVrPW0joOorIce2pyiNQAAAAAWLSuIzVVfWqir49LV+DePjXe27w9YrwFBXLnl+ZKj/N+aJ5Z3yo5/+jotEPHyUnUR2ZV14umZOUco+Y4bwSmFFkZXqBgeGGEFn1qaooL1M41prEjRHHfmq2eNSeVv0J734zc+pPXFp+MD/3UuPUn/On4U/aVaV2goCZC1ELNffTyvkdKxoxA1M51kaM2+6gtmIJF4+kEAAAAFoFCATmknwEAAABYtK4jNbk+NaWMsFwU87bkwGtaXOTm+tuU0sW8BQVa9L3JjZeG5UcsFJDaTuk62fnz9W27X5re41oUFGgxXus0utw5+5wXqy1KcIhyPWukyr41W+Ml53v71uRenmOkerVOL5mq10Qv/U0ugqFpb1L7NLChaVZTbjCfa9N77/1sWq+vtI6DeA9TKCCHSA0AAACARes6UgMAAAAgdhAhp+a6vqiJA2zZtLLC+XHKWVoJbSva6a1wVlvYqSZFLJey5p3nvLlimXQ2S14dq63KaFG/maRMWq4XzXoqb2rV7jFqKpylt7VOA9s+P5+mVlpfbg2pcv+dXJ+aure5t5JZ7fhj2lr7VD1rpO2Us6qUteR773u/phfGXCkkU6b7tEhbqhm/tq9MTYrUlFXXWhizP9CUVcg8ayitY59Uqin7N9XovXJbbg3nrQOL099fIwAAAAB2YE9NDntqAAAAACzaYiI1cYSwdH3qjiTm0srSEGapqpm3gWecBlZTJS1NI8tVMvNWSevkWS+lqXmbQsZj5CqSrW/bv0ramOls3vWV1luTypdqUeGsNP5cxqzclmvEKVU246xJGaltJNk6TWSuSmhzNW28aKkrrdPeaudu3RC0xeuxh1SqWj2saczHT6pPR1sEIjU5RGoAAAAALFrX16i5nqnePjVb0Z3kqv2OY34Ski4qN1cp8pOL7pS0KGSwyvxcUlwP4Ojoyum0J9sTH61296JZD+/bLJ87J928fpyJrLQuKFCKrNQUAEjXVxNNadGnprYowaEbt2eNtPVGq+lZ4/10ep9PtQ9lk7Hn/HSMsTeYD+3v4u2d07pAQQu199Gr5rGNjd1HJzZlIYO5Iklj/h7ZJ0I9ZeRrcrm/jkGkBgAAAMCiXdyPYgEAAIBFYU9NTtcXNYP71Hgn8vap8d5W04smVQqXejf959LKOpSmQR1tpWrl76T3uKEFBVqPV0qjKx1b09/Gm+Z3djxfeLt2/DmsLiWPUebpru1Zcynq33QlvqEmZU2qS+WoSRPZZ/ypUm1a9LNpkQrTOg2sd3Pd37F75/TwevTypsTN1WtqrvWleih4gK50fVEDAAAA4Cr21OSwpwYAAADAonUdqYmvRYf2qTlObrgt+v6aFhe8pV43uZSz2ippubS12uPi0GzjV0SawrTKpGr50758Fb/2Se/yzFXb98TbV6Z1ul3unFSLfi6l8XvTun9N3LNGko5vq6mMFqJvkkpordMwxqyg5j1uympTNWPU9gAaOm86d65KmORPzVrSfawZOx2/RWrkVFW+xqjkdSiV0cbs3dXi9w+61fVFDQAAAICrKBSQs8iLmlwvmvS2ra+9V/v7FArwXuF7CwqMeVzNM53cX4vGiPZDa7XaPnAVNbSp36Qe96bxRTv8x/n6xcS3te6P470fpWO9x5WU+tQsqQDA2HJ9a6p61kjbfWu8Y3iLgsRadNme8hPfMSMwc25mn6sHkFfrCFELQzd3t36+W/fRSfUeFel9fama9WLx2FMDAAAALMLVzRlT/DufmT3MzF5qZi83syfsuP0TzOy3zOzYzD6r8k67cFEDAAAAYC9mtpL0FEmfIul+kh5lZvdLDvtjSY+R9CNjr6fr9LOhfWpK15hx4YCtQgFpuNRbAKCUppbbsF+TvlYar/a4mV4FuRQzyZ9K5e3hkttgX5tWVjNebt27xs8d6z3OO3ZJrgBA6832c9rqW1N4H9T0rcn2rJHqUs5aFw2onav1cUPTVVqkGY25sd07bzp3zWb2sXv25HjnTW9r8dhOuWF/6BpqUr2k5W6fWFo/m8Xoak/NAyS9PITwCkkys2dJeoSkl1w9IITwqs1tZ/5X2BqRGgAAAAD7uoek10Tf37L52Swmu6gxs79iZs8xs7eb2avN7NFTzQ0AAAAs36R7aq43s5ujf4+d5j7WmTLx6CmSLku6u6T7S/oZM/vdEMKLcyfk+tSUMr1y0cgzVdKiMOgdx65TXkpvyx1Xqmp20vi43BrS0PFWxbPTr4+OtiOKJyenEx+t0jSr/Xu/bKd61fWp2a5k5quS1oK3r4w3jc57XO6clLfXjXc8rxaPew/ryFVFk9SgZ42kY9t93Jnz4nO8cyXfD0358B7nTa0ZO62shdZpYK3XW5Oy16KHS43S66JFRa2hVb56SaWKzbWmOfsc1RyHId4UQrihcPtrJd0r+v6em5/NYpKLGjO7k6TPlPThIYS3Sfp1M3uupM+XdKZSAgAAAIBUV3tqbpJ0XzO7j9YXM4+UNFsm1lTpZx8k6TiE8LLoZ78r6cMmmh8AAABAIyGEY0mPk/R8SX8g6UdDCC82syeb2cMlycw+1sxukfQPJH2vmWUztIaaKv3szpL+MvnZX0i6S3rgJl/vsZL07tHPc001U9nmm65lqpzP5r2tRbWyUtW1nNpqat7UtBGllba8aWpxClIp1at1lbSadDbvcWcfi91vU+9xJVOm4m3PO3/q2D5WR7tzG6qbb8ZaNOL0pl60bozX2pRpRkNTs6TpPixtkZrVWmneMVPsahvKtlxDuo6x30u9N75svb6aNfSSOjeJeHPG/EIIz5P0vORnT4y+vknrtLTRTRWpeZukuyY/u6ukt6YHhhBuDCHcEEK44d0mWRoAAACAJbMQwvlHDZ1kvafmzZI+LITwR5uf/YCk14UQsntqzOyNkt4u6U2jLxJLc714XWAbrwmkeE1gF14XSF0v6U4hhPeceyHnMbP/qfV6p/CmEMLDJpprsEkuaqTbG/IESV+kdfWz50n6+FL1s815N59TeQEXEK8LpHhNIMVrArvwukCK18RhmLL55pdJuqOkP5X0TElfet4FDQAAAACcZ7I+NSGEP5f0GVPNBwAAAOBimDJSU+vGuReALvG6QIrXBFK8JrALrwukeE0cgMn21AAAAADAGJYQqQEAAACALC5qAAAAACxatxc1ZvZXzOw5ZvZ2M3u1mT167jVhXmb2ODO72cxuNbOnz70ezM/MrjOzp21+R7zVzH7HzD5l7nVhXmb2Q2b2ejP7SzN7mZl90dxrQh/M7L5m9i4z+6G514L5mdmvbF4Pb9v8e+nca0K9bi9qJD1F0mVJd5f0uZK+x8w+bN4lYWavk/SNkr5/7oWgG0eSXiPpEyW9u6Svl/SjZnbvOReF2X2zpHuHEO4q6eGSvtHMPmbmNaEPT5F009yLQFceF0K48+bfB8+9GNTr8qLGzO4k6TMl/esQwttCCL8u6bmSPn/elWFOIYSfCCH8D0l/Nvda0IcQwttDCE8KIbwqhHAlhPDTkl4piT9gL7AQwotDCLde/Xbz7wNmXBI6YGaPlPQWSb8481IAjKDLixpJHyTpOITwsuhnvyuJSA2ALDO7u9a/P2jse8GZ2Xeb2Tsk/aGk10t63sxLwozM7K6Snizp8XOvBd35ZjN7k5n9bzN70NyLQb1eL2ruLOkvk5/9haS7zLAWAAtgZtdI+mFJzwgh/OHc68G8QghfpvX/M/6WpJ+QdGv5DBy4fyvpaSGEW+ZeCLryNZLeX9I9tO5V81NmRlR3oXq9qHmbpLsmP7urpLfOsBYAnTOzS5J+UOt9eI+beTnoRAjhZJO+fE9JXzr3ejAPM7u/pIdI+vaZl4LOhBBeGEJ4awjh1hDCMyT9b0mfOve6UOdo7gVkvEzSkZndN4TwR5uffaRIKQGQMDOT9DSti4p8agjhtpmXhP4ciT01F9mDJN1b0h+vf13ozpJWZna/EMJHz7gu9CdIsrkXgTpdRmpCCG/XOl3gyWZ2JzP7m5IeofUnsbigzOzIzO4gaaX1/5DuYGa9XphjOt8j6UMlfXoI4Z1zLwbzMrP3MrNHmtmdzWxlZp8s6VFic/hFdqPWF7X33/x7qqSfkfTJ8y0JczOzu5nZJ1/9W8LMPlfSJ0j6n3OvDXW6vKjZ+DJJd5T0p5KeKelLQwhEai62r5f0TklPkPR5m6+/ftYVYVZm9n6SvkTrP1TeEPUa+Nx5V4YZBa1TzW6R9GZJ3yrpK0MIz511VZhNCOEdIYQ3XP2ndYr7u0IIb5x7bZjVNVq3iXijpDdJ+nJJn5EUqcKCWAhh7jUAAAAAQLWeIzUAAAAAcC4uagAAAAAsGhc1AAAAABaNixoAAAAAi8ZFDQAAAIBF46IGAAAAwKJxUQNgsczsMWb26zPM+7fM7KVTzzuUmT3dzL5xwPnPNLPPyNx2bzMLS2qIa2a/aWYfNvc6AADDcVEDoGtm9ioze2fUWPNtZvZdE68hmNkHXv0+hPBrIYQPHnG+O2/u58+ONce+zOwjJH2kpJ+cey0NfaukJ8+9CADAcFzUAFiCTw8h3Dn697i5FzSyz5R0q6SHmtl7z72YjS+R9MNh4o7NtjbW/6ueK+lvd/QYAwAqcVED4GCY2YeY2c+b2Z+b2UvN7LM3P3+gmb3BzFbRsX/PzH5v8/UDzOw3zOwtZvZ6M/suM7t2c9uvbk753U305HPM7EFmdks01oea2a9szn+xmT08uu3pZvYUM/sZM3urmb3QzD7gnLvyhZKeKun3JH1ech9fZWZfbWa/Z2Z/YWb/3czuEN3+Lzf34XVm9kVplCkZ69PM7Hc26/4/m2hMzqdI+l/RuSsz+1Yze5OZvULS303Gfncze9pmLa81s2+8+vhvzv1Pm3NfaWaPi1PXNo/lN5nZ/5b0Dknvn3tuN8dft1nLH5vZn5jZU83sjpvbrjezn97cxz83s1+7epEUQniXpBdJ+uRzng8AQOe4qAFwEMzsTpJ+XtKPSHovSY+U9N1mdr8QwgslvV3Sg6NTHr05VpJOJP1zSddL+jhJf0fSl0lSCOETNsd85CZK9N+Tea+R9FOSfm4z75dL+mEzi9PTHinpGyS9h6SXS/qmwv14P0kPkvTDm39fsOOwz5b0MEn3kfQRkh6zOfdhkh4v6SGSPnAzTm6ej5L0/VpHYP6qpO+V9Fwzu27HsXfazBXvI/piSZ8m6aMk3SDps5LTni7peLOOj5L0SZK+KDr3UyTdX9JHS/qMHUv8fEmPlXQXSW9U5rndHPstkj5oM94HSrqHpCdubvsqSbdIek9Jd5f0ryTF0aY/0DqtDgCwYFzUAFiC/7H5pP3qvy/eccynSXpVCOG/hRCOQwi/LenHJf2Dze3PlPQoSTKzu0j61M3PFEJ4UQjhBZvzXqX1H/if6Fzb35B0Z0nfEkK4HEL4JUk/fXWujeeEEH4zhHCs9YXK/Qvjfb6k3wshvETSsyR92OYCJPadIYTXhRD+XOsLqqvjfbak/xZCeHEI4R2SnlSY57GSvjeE8MIQwkkI4Rlap7z9jR3H3m3z37dGP/tsSd8RQnjNZh3ffPUGM7u71o/vV4YQ3h5C+FNJ3671xcjVc/9zCOGWEMKbtb4oST19cz+Otb6A2/ncmplt7ss/DyH8eQjhrZL+XTTXbZLeR9L7hRBu2+yHii9q3hrdPwDAQi2mSg2AC+0zQgi/cM4x7yfpgWb2luhnR5J+cPP1j0j6P2b2pZL+vqTfCiG8WpLM7IMkfZvWEYd325z3Iufa/pqk14QQrkQ/e7XW0YKr3hB9/Q6tL4JyvkDSf5WkEMJrzex/aZ2O9tuF8f5atJabo9teU5jn/SR9oZl9efSza6OxYm/Z/Pcukt4VzRWP/+pk7GskvX59zSFp/SHa1ePTc3etM/5Z6bl9T62fsxdFc5mkq6mG/1Hri7uf29x+Ywghvoi6S3T/AAALxUUNgEPxGkn/K4Tw0F03hhBeYmav1jrtKU49k6Tv0fqi4VEhhLea2VfqbDpVzusk3cvMLkUXNu8r6WX73gEz+3hJ95X0tWb2VZsf30XSh5vZV2+iFiWvl3TP6Pt7FY59jaRvCiFkU+GuCiG83cz+n9YpXm+M5orHf99k7FslXZ9Zs2edcTQl+9xu9se8U9KHhRBeu2Ptb9U6Be2rzOzDJf2Smd0UQvjFzSEfKumHdswPAFgQ0s8AHIqflvRBZvb5ZnbN5t/HmtmHRsf8iKSvkPQJkn4s+vldJP2lpLeZ2YdI+tJk7D+R9P6ZeV+odbTkX27mfJCkT9c6dWxfX6j13pH7aZ1Sdn9JHy7pjlpfjJ3nRyX9w03hgneT9K8Lx/5XSf/E1kUUzMzuZGZ/d5Oat8vztJ2S96OS/pmZ3dPM3kPSE67eEEJ4vdZ7jP6Tmd3VzC6Z2QeY2SdG536Fmd3DzO4m6WvOuV/Z53ZzIflfJX27mb2XJG3G/eTN159mZh+4SVP7C633T13Z3HYHSR+j9WMOAFgwLmoALMFP2XafmuekB2w+kf8krfdSvE7rFK1/Lyne+P5Mrf8w/6UQwpuin3+11tGbt2r9B/JWMQCt05eesdnP89nxDSGEy1pfxHyKpDdJ+m5JXxBC+MN97uDmD+zPlvRfQghviP69Uus0qy88b4wQws9K+k5Jv6x1QYIXbG66dcexN2u9Yf+7JL15c/xjCsPfKOlz7TTH679Ker6k35X0W5J+Ijn+C7ROZ3vJZvxna7235eq5P6d1dbff1vqC6VjrC45d9+u85/Zrrt5fM/tLSb8g6Wqhhvtuvn+bpN+Q9N0hhF/e3Pbpkn4lhPC6wv0GACyATdxyAAAwkU2U6v9Kus6RuuYZ70ck/WgI4X8MHSsZ91MkPTWE8H4tx3XM+0JJ/ziE8H+nnBcA0B4XNQBwQMzs72kd+Xg3Sc+QdCWE8BmzLiqx6SHzt7WO1txd60pmLwghfOWc6wIALBfpZwBwWL5E0p9K+n9ap3Ol+4N6YFr37Xmz1ulnf6DTvjIAAOyNSA0AAACARSNSAwAAAGDRuKgBAAAAsGhc1AAAAABYNC5qAAAAACwaFzUAAAAAFo2LGgAAAACL9v8D3t7k6fQU2CwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Display the results\n",
    "\n",
    "\n",
    "im = plt.pcolor(theta, height, beam_spreading_loss, cmap=\"jet\", shading = 'auto')\n",
    "\n",
    "cbar = plt.colorbar(im, orientation='vertical')\n",
    "\n",
    "cbar.set_label(\"(dB)\")\n",
    "\n",
    "\n",
    "\n",
    "# Set the plot title and labels\n",
    "\n",
    "plt.title('Beam Spreading Loss', size=14)\n",
    "\n",
    "plt.xlabel('Elevation Angle (degrees)', size=12)\n",
    "\n",
    "plt.ylabel('Height (km)', size=12)\n",
    "\n",
    "\n",
    "# Set the tick label size\n",
    "\n",
    "plt.tick_params(labelsize=12)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
